{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# FSRS4Anki Optimizer mini-batch_power-difficulty"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![open in colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/open-spaced-repetition/fsrs4anki/blob/main/archive/experiment/mini-batch_power-difficulty.ipynb)\n",
    "\n",
    "↑ Click the above button to open the optimizer on Google Colab.\n",
    "\n",
    "> If you can't see the button and are located in the Chinese Mainland, please use a proxy or VPN."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Upload your **Anki Deck Package (.apkg)** file or **Anki Collection Package (.colpkg)** file on the `Left sidebar -> Files`, drag and drop your file in the current directory (not the `sample_data` directory). \n",
    "\n",
    "No need to include media. Need to include scheduling information. \n",
    "\n",
    "> If you use the latest version of Anki, please check the box `Support older Anki versions (slower/larger files)` when you export.\n",
    "\n",
    "You can export it via `File -> Export...` or `Ctrl + E` in the main window of Anki.\n",
    "\n",
    "Then replace the `filename` with yours in the next code cell. And set the `timezone` and `next_day_starts_at` which can be found in your preferences of Anki.\n",
    "\n",
    "After that, just run all (`Runtime -> Run all` or `Ctrl + F9`) and wait for minutes. You can see the optimal parameters in section **2.3 Result**. Copy them, replace the parameters in `fsrs4anki_scheduler.js`, and paste them into the custom scheduling of your deck options (require Anki version >= 2.1.55).\n",
    "\n",
    "**NOTE**: The default output is generated from my review logs. If you find the output is the same as mine, maybe your notebook hasn't run there.\n",
    "\n",
    "**Contribute to SRS Research**: If you want to share your data with me, please fill this form: https://forms.gle/KaojsBbhMCytaA7h8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here are some settings that you need to replace before running this optimizer.\n",
    "\n",
    "filename = \"../collection-2022-09-18@13-21-58.colpkg\"\n",
    "# If you upload deck file, replace it with your deck filename. E.g., ALL__Learning.apkg\n",
    "# If you upload collection file, replace it with your colpgk filename. E.g., collection-2022-09-18@13-21-58.colpkg\n",
    "\n",
    "# Replace it with your timezone. I'm in China, so I use Asia/Shanghai.\n",
    "# You can find your timezone here: https://gist.github.com/heyalexej/8bf688fd67d7199be4a1682b3eec7568\n",
    "timezone = 'Asia/Shanghai'\n",
    "\n",
    "# Replace it with your Anki's setting in Preferences -> Scheduling.\n",
    "next_day_starts_at = 4\n",
    "\n",
    "# Replace it if you don't want the optimizer to use the review logs before a specific date.\n",
    "revlog_start_date = \"2006-10-05\""
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 Build dataset"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Extract Anki collection & deck file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Extract successfully!\n"
     ]
    }
   ],
   "source": [
    "import zipfile\n",
    "import sqlite3\n",
    "import time\n",
    "from tqdm import notebook\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from datetime import timedelta, datetime\n",
    "import matplotlib.pyplot as plt\n",
    "import math\n",
    "import sys\n",
    "import torch\n",
    "from torch import nn\n",
    "from sklearn.utils import shuffle\n",
    "# Extract the collection file or deck file to get the .anki21 database.\n",
    "with zipfile.ZipFile(f'./{filename}', 'r') as zip_ref:\n",
    "    zip_ref.extractall('./')\n",
    "    print(\"Extract successfully!\")\n",
    "\n",
    "\n",
    "notebook.tqdm.pandas()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Create time-series feature & analysis\n",
    "\n",
    "The following code cell will extract the review logs from your Anki collection and preprocess them to a trainset which is saved in [./revlog_history.tsv](./revlog_history.tsv).\n",
    "\n",
    "The time-series features are important in optimizing the model's parameters. For more detail, please see my paper: https://www.maimemo.com/paper/\n",
    "\n",
    "Then it will generate a concise analysis for your review logs. \n",
    "\n",
    "- The `r_history` is the history of ratings on each review. `3,3,3,1` means that you press `Good, Good, Good, Again`. It only contains the first rating for each card on the review date, i.e., when you press `Again` in review and  `Good` in relearning steps 10min later, only `Again` will be recorded.\n",
    "- The `avg_interval` is the actual average interval after you rate your cards as the `r_history`. It could be longer than the interval given by Anki's built-in scheduler because you reviewed some overdue cards.\n",
    "- The `avg_retention` is the average retention after you press as the `r_history`. `Again` counts as failed recall, and `Hard, Good and Easy` count as successful recall. Retention is the percentage of your successful recall.\n",
    "- The `stability` is the estimated memory state variable, which is an approximate interval that leads to 90% retention.\n",
    "- The `factor` is `stability / previous stability`.\n",
    "- The `group_cnt` is the number of review logs that have the same `r_history`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "revlog.csv saved.\n",
      "Trainset saved.\n",
      "Retention calculated.\n"
     ]
    },
    {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Stability calculated.\n"
     ]
    },
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "      r_history  avg_interval  avg_retention  stability  factor  group_cnt\n",
      "              1           1.7          0.765        1.0     inf       7997\n",
      "            1,3           3.9          0.877        4.2    4.20       4174\n",
      "          1,3,3           8.5          0.882        9.1    2.17       2711\n",
      "        1,3,3,3          17.8          0.860       13.8    1.52       1619\n",
      "      1,3,3,3,3          36.4          0.833       22.3    1.62        845\n",
      "    1,3,3,3,3,3          74.7          0.861       36.4    1.63        402\n",
      "  1,3,3,3,3,3,3         118.2          0.895       38.5    1.06        182\n",
      "              2           1.0          0.901        1.1     inf        240\n",
      "            2,3           3.5          0.946        8.3    7.55        201\n",
      "          2,3,3          11.1          0.890        7.1    0.86        160\n",
      "              3           1.5          0.962        5.4     inf       9134\n",
      "            3,3           3.9          0.966       15.2    2.81       6588\n",
      "          3,3,3           9.0          0.960       23.5    1.55       5166\n",
      "        3,3,3,3          19.1          0.941       43.5    1.85       3542\n",
      "      3,3,3,3,3          35.9          0.931       53.0    1.22       1976\n",
      "    3,3,3,3,3,3          65.0          0.928       97.6    1.84       1149\n",
      "  3,3,3,3,3,3,3         100.9          0.947      142.9    1.46        537\n",
      "3,3,3,3,3,3,3,3         133.6          0.990     1821.9   12.75        131\n",
      "              4           3.8          0.966       12.1     inf      11599\n",
      "            4,3           8.1          0.975       39.0    3.22       7515\n",
      "          4,3,3          17.8          0.963       56.5    1.45       5307\n",
      "        4,3,3,3          32.1          0.952       82.6    1.46       3032\n",
      "      4,3,3,3,3          46.3          0.956      125.4    1.52       1380\n",
      "    4,3,3,3,3,3          67.5          0.956      115.3    0.92        534\n",
      "  4,3,3,3,3,3,3          78.3          0.978      117.5    1.02        253\n",
      "4,3,3,3,3,3,3,3         114.1          0.984      177.8    1.51        166\n",
      "Analysis saved!\n"
     ]
    }
   ],
   "source": [
    "if os.path.isfile(\"collection.anki21b\"):\n",
    "    os.remove(\"collection.anki21b\")\n",
    "    raise Exception(\n",
    "        \"Please export the file with `support older Anki versions` if you use the latest version of Anki.\")\n",
    "elif os.path.isfile(\"collection.anki21\"):\n",
    "    con = sqlite3.connect(\"collection.anki21\")\n",
    "elif os.path.isfile(\"collection.anki2\"):\n",
    "    con = sqlite3.connect(\"collection.anki2\")\n",
    "else:\n",
    "    raise Exception(\"Collection not exist!\")\n",
    "cur = con.cursor()\n",
    "res = cur.execute(\"SELECT * FROM revlog\")\n",
    "revlog = res.fetchall()\n",
    "\n",
    "df = pd.DataFrame(revlog)\n",
    "df.columns = ['id', 'cid', 'usn', 'r', 'ivl',\n",
    "              'last_lvl', 'factor', 'time', 'type']\n",
    "df = df[(df['cid'] <= time.time() * 1000) &\n",
    "        (df['id'] <= time.time() * 1000) &\n",
    "        (df['r'] > 0)].copy()\n",
    "df['create_date'] = pd.to_datetime(df['cid'] // 1000, unit='s')\n",
    "df['create_date'] = df['create_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df['review_date'] = pd.to_datetime(df['id'] // 1000, unit='s')\n",
    "df['review_date'] = df['review_date'].dt.tz_localize(\n",
    "    'UTC').dt.tz_convert(timezone)\n",
    "df.drop(df[df['review_date'].dt.year < 2006].index, inplace=True)\n",
    "df.sort_values(by=['cid', 'id'], inplace=True, ignore_index=True)\n",
    "type_sequence = np.array(df['type'])\n",
    "time_sequence = np.array(df['time'])\n",
    "df.to_csv(\"revlog.csv\", index=False)\n",
    "print(\"revlog.csv saved.\")\n",
    "df = df[(df['type'] != 3) | (df['factor'] != 0)].copy()\n",
    "df['real_days'] = df['review_date'] - timedelta(hours=next_day_starts_at)\n",
    "df['real_days'] = pd.DatetimeIndex(df['real_days'].dt.floor('D', ambiguous='infer', nonexistent='shift_forward')).to_julian_date()\n",
    "df.drop_duplicates(['cid', 'real_days'], keep='first', inplace=True)\n",
    "df['delta_t'] = df.real_days.diff()\n",
    "df.dropna(inplace=True)\n",
    "df['delta_t'] = df['delta_t'].astype(dtype=int)\n",
    "\n",
    "df['i'] = df.groupby('cid').cumcount() + 1\n",
    "df.loc[df['i'] == 1, 'delta_t'] = 0\n",
    "\n",
    "\n",
    "df = df.groupby('cid').filter(lambda group: group['type'].iloc[0] == 0)\n",
    "df['prev_type'] = df.groupby('cid')['type'].shift(1).fillna(0).astype(int)\n",
    "df['helper'] = (df['type'] == 0) & ((df['prev_type'] == 1) | (df['prev_type'] == 2)) & (df['i'] > 1)\n",
    "df['helper'] = df['helper'].astype(int)\n",
    "df['helper'] = df.groupby('cid')['helper'].cumsum()\n",
    "df = df[df['helper'] == 0]\n",
    "del df['prev_type']\n",
    "del df['helper']\n",
    "from itertools import accumulate\n",
    "\n",
    "def cum_concat(x):\n",
    "    return list(accumulate(x))\n",
    "\n",
    "t_history = df.groupby('cid', group_keys=False)['delta_t'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['t_history']=[','.join(map(str, item[:-1])) for sublist in t_history for item in sublist]\n",
    "r_history = df.groupby('cid', group_keys=False)['r'].apply(lambda x: cum_concat([[i] for i in x]))\n",
    "df['r_history']=[','.join(map(str, item[:-1])) for sublist in r_history for item in sublist]\n",
    "df = df[df['id'] >= time.mktime(datetime.strptime(revlog_start_date, \"%Y-%m-%d\").timetuple()) * 1000]\n",
    "df.to_csv('revlog_history.tsv', sep=\"\\t\", index=False)\n",
    "print(\"Trainset saved.\")\n",
    "\n",
    "df['y'] = df['r'].map(lambda x: {1: 0, 2: 1, 3: 1, 4: 1}[x])\n",
    "df['retention'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['y'].transform('mean')\n",
    "df['total_cnt'] = df.groupby(by=['r_history', 'delta_t'], group_keys=False)['id'].transform('count')\n",
    "print(\"Retention calculated.\")\n",
    "df = df.drop(columns=['id', 'cid', 'usn', 'ivl', 'last_lvl', 'factor', 'time', 'type', 'create_date', 'review_date', 'real_days', 'r', 't_history', 'y'])\n",
    "df.drop_duplicates(inplace=True)\n",
    "df['retention'] = df['retention'].map(lambda x: max(min(0.99, x), 0.01))\n",
    "\n",
    "def cal_stability(group: pd.DataFrame) -> pd.DataFrame:\n",
    "    group_cnt = sum(group['total_cnt'])\n",
    "    if group_cnt < 10:\n",
    "        return pd.DataFrame()\n",
    "    group['group_cnt'] = group_cnt\n",
    "    if group['i'].values[0] > 1:\n",
    "        r_ivl_cnt = sum(group['delta_t'] * group['retention'].map(np.log) * pow(group['total_cnt'], 2))\n",
    "        ivl_ivl_cnt = sum(group['delta_t'].map(lambda x: x ** 2) * pow(group['total_cnt'], 2))\n",
    "        group['stability'] = round(np.log(0.9) / (r_ivl_cnt / ivl_ivl_cnt), 1)\n",
    "    else:\n",
    "        group['stability'] = 0.0\n",
    "    group['avg_retention'] = round(sum(group['retention'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 3)\n",
    "    group['avg_interval'] = round(sum(group['delta_t'] * pow(group['total_cnt'], 2)) / sum(pow(group['total_cnt'], 2)), 1)\n",
    "    del group['total_cnt']\n",
    "    del group['retention']\n",
    "    del group['delta_t']\n",
    "    return group\n",
    "\n",
    "df = df.groupby(by=['r_history'], group_keys=False).progress_apply(cal_stability)\n",
    "print(\"Stability calculated.\")\n",
    "df.reset_index(drop = True, inplace = True)\n",
    "df.drop_duplicates(inplace=True)\n",
    "df.sort_values(by=['r_history'], inplace=True, ignore_index=True)\n",
    "\n",
    "if df.shape[0] > 0:\n",
    "    for idx in notebook.tqdm(df.index):\n",
    "        item = df.loc[idx]\n",
    "        index = df[(df['i'] == item['i'] + 1) & (df['r_history'].str.startswith(item['r_history']))].index\n",
    "        df.loc[index, 'last_stability'] = item['stability']\n",
    "    df['factor'] = round(df['stability'] / df['last_stability'], 2)\n",
    "    df = df[(df['i'] >= 2) & (df['group_cnt'] >= 100)]\n",
    "    df['last_recall'] = df['r_history'].map(lambda x: x[-1])\n",
    "    df = df[df.groupby(['i', 'r_history'], group_keys=False)['group_cnt'].transform(max) == df['group_cnt']]\n",
    "    df.to_csv('./stability_for_analysis.tsv', sep='\\t', index=None)\n",
    "    print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "    print(df[df['r_history'].str.contains(r'^[1-4][^124]*$', regex=True)][['r_history', 'avg_interval', 'avg_retention', 'stability', 'factor', 'group_cnt']].to_string(index=False))\n",
    "    print(\"Analysis saved!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 Optimize parameter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1 Define the model\n",
    "\n",
    "FSRS is a time-series model for predicting memory states."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_w = [1, 1, 7, 2, 2, 0.2, 1.4, 0.2, 0.8, 2, 0.2, 0.2, 0.2, 1]\n",
    "'''\n",
    "w[0]: initial_stability_for_again_answer\n",
    "w[1]: initial_stability_step_per_rating\n",
    "w[2]: initial_difficulty_for_good_answer\n",
    "w[3]: initial_difficulty_step_per_rating\n",
    "w[4]: next_difficulty_step_per_rating\n",
    "w[5]: next_difficulty_reversion_to_mean_speed (used to avoid ease hell)\n",
    "w[6]: next_stability_factor_after_success\n",
    "w[7]: next_stability_stabilization_decay_after_success\n",
    "w[8]: next_stability_retrievability_gain_after_success\n",
    "w[9]: next_stability_factor_after_failure\n",
    "w[10]: next_stability_difficulty_decay_after_success\n",
    "w[11]: next_stability_stability_gain_after_failure\n",
    "w[12]: next_stability_retrievability_gain_after_failure\n",
    "For more details about the parameters, please see: \n",
    "https://github.com/open-spaced-repetition/fsrs4anki/wiki/Free-Spaced-Repetition-Scheduler\n",
    "'''\n",
    "\n",
    "\n",
    "class FSRS(nn.Module):\n",
    "    def __init__(self, w):\n",
    "        super(FSRS, self).__init__()\n",
    "        self.w = nn.Parameter(torch.tensor(w))\n",
    "\n",
    "    def stability_after_success(self, state, new_d, r):\n",
    "        new_s = state[:,0] * (1 + torch.exp(self.w[6]) *\n",
    "                        (10 * torch.pow(new_d, -self.w[13])) *\n",
    "                        torch.pow(state[:,0], -self.w[7]) *\n",
    "                        (torch.exp((1 - r) * self.w[8]) - 1))\n",
    "        return new_s\n",
    "    \n",
    "    def stability_after_failure(self, state, new_d, r):\n",
    "        new_s = self.w[9] * torch.pow(new_d, -self.w[10]) * torch.pow(\n",
    "            state[:,0], self.w[11]) * torch.exp((1 - r) * self.w[12])\n",
    "        return new_s\n",
    "\n",
    "    def step(self, X, state):\n",
    "        '''\n",
    "        :param X: shape[batch_size, 2], X[:,0] is elapsed time, X[:,1] is rating\n",
    "        :param state: shape[batch_size, 2], state[:,0] is stability, state[:,1] is difficulty\n",
    "        :return state:\n",
    "        '''\n",
    "        if torch.equal(state, torch.zeros_like(state)):\n",
    "            # first learn, init memory states\n",
    "            new_s = self.w[0] + self.w[1] * (X[:,1] - 1)\n",
    "            new_d = self.w[2] - self.w[3] * (X[:,1] - 3)\n",
    "            new_d = new_d.clamp(1, 15)\n",
    "        else:\n",
    "            r = torch.exp(np.log(0.9) * X[:,0] / state[:,0])\n",
    "            new_d = state[:,1] - self.w[4] * (X[:,1] - 3)\n",
    "            new_d = self.mean_reversion(self.w[2], new_d)\n",
    "            new_d = new_d.clamp(1, 15)\n",
    "            condition = X[:,1] > 1\n",
    "            new_s = torch.where(condition, self.stability_after_success(state, new_d, r), self.stability_after_failure(state, new_d, r))\n",
    "        return torch.stack([new_s, new_d], dim=1)\n",
    "\n",
    "    def forward(self, inputs, state=None):\n",
    "        '''\n",
    "        :param inputs: shape[seq_len, batch_size, 2]\n",
    "        '''\n",
    "        if state is None:\n",
    "            state = torch.zeros((inputs.shape[1], 2))\n",
    "        else:\n",
    "            state, = state\n",
    "        outputs = []\n",
    "        for X in inputs:\n",
    "            state = self.step(X, state)\n",
    "            outputs.append(state)\n",
    "        return torch.stack(outputs), state\n",
    "\n",
    "    def mean_reversion(self, init, current):\n",
    "        return self.w[5] * init + (1-self.w[5]) * current\n",
    "\n",
    "\n",
    "class WeightClipper(object):\n",
    "    def __init__(self, frequency=1):\n",
    "        self.frequency = frequency\n",
    "\n",
    "    def __call__(self, module):\n",
    "        if hasattr(module, 'w'):\n",
    "            w = module.w.data\n",
    "            w[0] = w[0].clamp(0.1, 10)\n",
    "            w[1] = w[1].clamp(0.1, 5)\n",
    "            w[2] = w[2].clamp(1, 15)\n",
    "            w[3] = w[3].clamp(0.1, 5)\n",
    "            w[4] = w[4].clamp(0.1, 5)\n",
    "            w[5] = w[5].clamp(0.05, 0.5)\n",
    "            w[6] = w[6].clamp(0, 2)\n",
    "            w[7] = w[7].clamp(0.15, 0.8)\n",
    "            w[8] = w[8].clamp(0.01, 1.5)\n",
    "            w[9] = w[9].clamp(0.5, 5)\n",
    "            w[10] = w[10].clamp(0.01, 2)\n",
    "            w[11] = w[11].clamp(0.01, 0.9)\n",
    "            w[12] = w[12].clamp(0.01, 2)\n",
    "            w[13] = w[13].clamp(0.1, 2)\n",
    "            module.w.data = w\n",
    "\n",
    "def lineToTensor(line):\n",
    "    ivl = line[0].split(',')\n",
    "    response = line[1].split(',')\n",
    "    tensor = torch.zeros(len(response), 2)\n",
    "    for li, response in enumerate(response):\n",
    "        tensor[li][0] = int(ivl[li])\n",
    "        tensor[li][1] = int(response)\n",
    "    return tensor\n",
    "\n",
    "from torch.utils.data import Dataset, DataLoader, Sampler\n",
    "from torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n",
    "from typing import List\n",
    "\n",
    "class RevlogDataset(Dataset):\n",
    "    def __init__(self, dataframe):\n",
    "        padded = pad_sequence(dataframe['tensor'].to_list(), batch_first=True, padding_value=0)\n",
    "        self.x_train = padded.int()\n",
    "        self.t_train = torch.tensor(dataframe['delta_t'].values, dtype=torch.int)\n",
    "        self.y_train = torch.tensor(dataframe['y'].values, dtype=torch.float)\n",
    "        self.seq_len = torch.tensor(dataframe['tensor'].map(len).values, dtype=torch.long)\n",
    "\n",
    "    def __getitem__(self, idx):\n",
    "        return self.x_train[idx], self.t_train[idx], self.y_train[idx], self.seq_len[idx]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.y_train)\n",
    "    \n",
    "class RevlogSampler(Sampler[List[int]]):\n",
    "    def __init__(self, data_source, batch_size):\n",
    "        self.data_source = data_source\n",
    "        self.batch_size = batch_size\n",
    "        lengths = np.array(data_source.seq_len)\n",
    "        indices = np.argsort(lengths)\n",
    "        full_batches, remainder = divmod(indices.size, self.batch_size)\n",
    "        if full_batches > 0:\n",
    "            self.batch_indices = np.split(indices[:-remainder], full_batches)\n",
    "        else:\n",
    "            self.batch_indices = []\n",
    "        if remainder:\n",
    "            self.batch_indices.append(indices[-remainder:])\n",
    "        self.batch_nums = len(self.batch_indices)\n",
    "        # seed = int(torch.empty((), dtype=torch.int64).random_().item())\n",
    "        seed = 2023\n",
    "        self.generator = torch.Generator()\n",
    "        self.generator.manual_seed(seed)\n",
    "\n",
    "    def __iter__(self):\n",
    "        yield from [self.batch_indices[idx] for idx in torch.randperm(self.batch_nums, generator=self.generator).tolist()]\n",
    "\n",
    "    def __len__(self):\n",
    "        return len(self.data_source)\n",
    "\n",
    "\n",
    "def collate_fn(batch):\n",
    "    sequences, delta_ts, labels, seq_lens = zip(*batch)\n",
    "    sequences_packed = pack_padded_sequence(torch.stack(sequences, dim=1), lengths=torch.stack(seq_lens), batch_first=False, enforce_sorted=False)\n",
    "    sequences_padded, length = pad_packed_sequence(sequences_packed, batch_first=False)\n",
    "    sequences_padded = torch.as_tensor(sequences_padded)\n",
    "    seq_lens = torch.as_tensor(length)\n",
    "    delta_ts = torch.as_tensor(delta_ts)\n",
    "    labels = torch.as_tensor(labels)\n",
    "    return sequences_padded, delta_ts, labels, seq_lens\n",
    "\n",
    "class Optimizer(object):\n",
    "    def __init__(self, train_set, test_set, n_epoch=1, lr=1e-2, batch_size=256) -> None:\n",
    "        self.model = FSRS(init_w)\n",
    "        self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)\n",
    "        self.clipper = WeightClipper()\n",
    "        self.batch_size = batch_size\n",
    "        self.build_dataset(train_set, test_set)\n",
    "        self.n_epoch = n_epoch\n",
    "        self.batch_nums = self.next_train_data_loader.batch_sampler.batch_nums\n",
    "        self.scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer, T_max=self.batch_nums * n_epoch)\n",
    "        self.avg_train_losses = []\n",
    "        self.avg_eval_losses = []\n",
    "        self.loss_fn = nn.BCELoss(reduction='sum')\n",
    "\n",
    "    def build_dataset(self, train_set, test_set):\n",
    "        pre_train_set = train_set[train_set['i'] == 2]\n",
    "        self.pre_train_set = RevlogDataset(pre_train_set)\n",
    "        sampler = RevlogSampler(self.pre_train_set, batch_size=self.batch_size)\n",
    "        self.pre_train_data_loader = DataLoader(self.pre_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        next_train_set = train_set[train_set['i'] > 2]\n",
    "        self.next_train_set = RevlogDataset(next_train_set)\n",
    "        sampler = RevlogSampler(self.next_train_set, batch_size=self.batch_size)\n",
    "        self.next_train_data_loader = DataLoader(self.next_train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.train_set = RevlogDataset(train_set)\n",
    "        sampler = RevlogSampler(self.train_set, batch_size=self.batch_size)\n",
    "        self.train_data_loader = DataLoader(self.train_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "\n",
    "        self.test_set = RevlogDataset(test_set)\n",
    "        sampler = RevlogSampler(self.test_set, batch_size=self.batch_size)\n",
    "        self.test_data_loader = DataLoader(self.test_set, batch_sampler=sampler, collate_fn=collate_fn)\n",
    "        print(\"dataset built\")\n",
    "\n",
    "    def train(self):\n",
    "        # pretrain\n",
    "        self.eval()\n",
    "        pbar = notebook.tqdm(desc=\"pre-train\", colour=\"red\", total=len(self.pre_train_data_loader))\n",
    "\n",
    "        for i, batch in enumerate(self.pre_train_data_loader):\n",
    "            self.model.train()\n",
    "            self.optimizer.zero_grad()\n",
    "            sequences, delta_ts, labels, seq_lens = batch\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences)\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            loss = self.loss_fn(retentions, labels)\n",
    "            loss.backward()\n",
    "            self.optimizer.step()\n",
    "            self.model.apply(self.clipper)\n",
    "            pbar.update(n=real_batch_size)\n",
    "\n",
    "        pbar.close()\n",
    "        for name, param in self.model.named_parameters():\n",
    "            print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "\n",
    "        epoch_len = len(self.next_train_data_loader)\n",
    "        pbar = notebook.tqdm(desc=\"train\", colour=\"red\", total=epoch_len*self.n_epoch)\n",
    "        print_len = max(self.batch_nums*self.n_epoch // 10, 1)\n",
    "\n",
    "\n",
    "        for k in range(self.n_epoch):\n",
    "            self.eval()\n",
    "            for i, batch in enumerate(self.next_train_data_loader):\n",
    "                self.model.train()\n",
    "                self.optimizer.zero_grad()\n",
    "                sequences, delta_ts, labels, seq_lens = batch\n",
    "                real_batch_size = seq_lens.shape[0]\n",
    "                outputs, _ = self.model(sequences)\n",
    "                stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "                retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "                loss = self.loss_fn(retentions, labels)\n",
    "                loss.backward()\n",
    "                for param in self.model.parameters():\n",
    "                    param.grad[:2] = torch.zeros(2)\n",
    "                self.optimizer.step()\n",
    "                self.scheduler.step()\n",
    "                self.model.apply(self.clipper)\n",
    "                pbar.update(real_batch_size)\n",
    "\n",
    "                if (k * self.batch_nums + i + 1) % print_len == 0:\n",
    "                    print(f\"iteration: {k * epoch_len + (i + 1) * self.batch_size}\")\n",
    "                    for name, param in self.model.named_parameters():\n",
    "                        print(f\"{name}: {list(map(lambda x: round(float(x), 4),param))}\")\n",
    "        pbar.close()\n",
    "                \n",
    "        self.eval()\n",
    "\n",
    "        w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "        return w\n",
    "\n",
    "    def eval(self):\n",
    "        self.model.eval()\n",
    "        with torch.no_grad():\n",
    "            sequences, delta_ts, labels, seq_lens = self.train_set.x_train, self.train_set.t_train, self.train_set.y_train, self.train_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            tran_loss = self.loss_fn(retentions, labels)/len(self.train_set)\n",
    "            self.avg_train_losses.append(tran_loss)\n",
    "            print(f\"Loss in trainset: {tran_loss:.4f}\")\n",
    "            \n",
    "            sequences, delta_ts, labels, seq_lens = self.test_set.x_train, self.test_set.t_train, self.test_set.y_train, self.test_set.seq_len\n",
    "            real_batch_size = seq_lens.shape[0]\n",
    "            outputs, _ = self.model(sequences.transpose(0, 1))\n",
    "            stabilities = outputs[seq_lens-1, torch.arange(real_batch_size), 0]\n",
    "            retentions = torch.exp(np.log(0.9) * delta_ts / stabilities)\n",
    "            test_loss = self.loss_fn(retentions, labels)/len(self.test_set)\n",
    "            self.avg_eval_losses.append(test_loss)\n",
    "            print(f\"Loss in testset: {test_loss:.4f}\")\n",
    "\n",
    "            w = list(map(lambda x: round(float(x), 4), dict(self.model.named_parameters())['w'].data))\n",
    "\n",
    "            weighted_loss = (tran_loss * len(self.train_set) + test_loss * len(self.test_set)) / (len(self.train_set) + len(self.test_set))\n",
    "\n",
    "            return weighted_loss, w\n",
    "\n",
    "    def plot(self):\n",
    "        plt.plot(self.avg_train_losses, label='train')\n",
    "        plt.plot(self.avg_eval_losses, label='test')\n",
    "        plt.xlabel('epoch')\n",
    "        plt.ylabel('loss')\n",
    "        plt.legend()\n",
    "        plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Train the model\n",
    "\n",
    "The [./revlog_history.tsv](./revlog_history.tsv) generated before will be used for training the FSRS model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3d6a4a70d1a3497a8b04783c48717f94",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/224893 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tensorized!\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/miniforge/base/envs/fsrs4anki/lib/python3.9/site-packages/sklearn/model_selection/_split.py:909: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 178992 TEST: 45901\n",
      "dataset built\n",
      "Loss in trainset: 0.3780\n",
      "Loss in testset: 0.3213\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad657e22cd5042e580ca55c020d208ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/17371 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0151, 1.6196, 7.0, 2.0, 2.0, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e0ea03c2bd774b8b93d2e05e4189b3fc",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/808105 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3712\n",
      "Loss in testset: 0.3102\n",
      "iteration: 80896\n",
      "w: [1.0871, 1.8746, 4.0572, 2.6489, 2.3054, 0.0596, 1.9284, 0.2533, 1.3848, 1.9228, 0.358, 0.4275, 0.9971, 1.1731]\n",
      "iteration: 161792\n",
      "w: [1.0871, 1.8746, 2.9256, 1.5263, 2.2218, 0.0535, 1.9581, 0.2088, 1.458, 1.7988, 0.461, 0.5537, 0.7247, 1.3553]\n",
      "Loss in trainset: 0.3271\n",
      "Loss in testset: 0.2747\n",
      "iteration: 242517\n",
      "w: [1.0871, 1.8746, 2.3616, 1.7718, 2.0671, 0.0933, 1.9897, 0.2296, 1.491, 1.6859, 0.4464, 0.4808, 0.8392, 1.3279]\n",
      "iteration: 323413\n",
      "w: [1.0871, 1.8746, 2.2324, 1.9334, 1.8492, 0.1128, 1.9073, 0.2736, 1.4234, 1.7007, 0.371, 0.5683, 0.8471, 1.3699]\n",
      "Loss in trainset: 0.3253\n",
      "Loss in testset: 0.2724\n",
      "iteration: 404138\n",
      "w: [1.0871, 1.8746, 1.9185, 1.6626, 1.6972, 0.1231, 1.9313, 0.2716, 1.4319, 1.5932, 0.4289, 0.5261, 0.8006, 1.3749]\n",
      "iteration: 485034\n",
      "w: [1.0871, 1.8746, 1.8967, 1.7755, 1.5864, 0.1038, 1.9369, 0.2498, 1.414, 1.625, 0.3598, 0.5843, 0.7702, 1.4522]\n",
      "Loss in trainset: 0.3252\n",
      "Loss in testset: 0.2723\n",
      "iteration: 565759\n",
      "w: [1.0871, 1.8746, 1.8194, 1.6027, 1.5938, 0.085, 1.9203, 0.2864, 1.3916, 1.5973, 0.3487, 0.5389, 0.7202, 1.4606]\n",
      "iteration: 646655\n",
      "w: [1.0871, 1.8746, 1.8154, 1.6486, 1.5365, 0.0891, 1.9268, 0.282, 1.389, 1.5414, 0.3767, 0.554, 0.6909, 1.4845]\n",
      "Loss in trainset: 0.3248\n",
      "Loss in testset: 0.2723\n",
      "iteration: 727380\n",
      "w: [1.0871, 1.8746, 1.7906, 1.619, 1.5097, 0.1021, 1.948, 0.2741, 1.4068, 1.5545, 0.3575, 0.5462, 0.7062, 1.4748]\n",
      "iteration: 808276\n",
      "w: [1.0871, 1.8746, 1.7966, 1.6249, 1.5185, 0.0896, 1.9399, 0.2802, 1.3983, 1.5495, 0.3615, 0.5492, 0.7019, 1.4843]\n",
      "Loss in trainset: 0.3246\n",
      "Loss in testset: 0.2722\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 182580 TEST: 42313\n",
      "dataset built\n",
      "Loss in trainset: 0.3570\n",
      "Loss in testset: 0.4070\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e983e240c8d441f79030a81baec8ab1c",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/20973 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.8037, 1.9065, 7.0, 2.0, 2.0, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a3ad8f586a0f4ed183cad3b8d88aecf6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/808035 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3423\n",
      "Loss in testset: 0.4009\n",
      "iteration: 80896\n",
      "w: [1.9164, 2.0446, 3.8754, 2.7683, 2.2342, 0.05, 1.9315, 0.15, 1.3188, 1.8038, 0.515, 0.5375, 0.9458, 1.2325]\n",
      "iteration: 161792\n",
      "w: [1.9164, 2.0446, 2.895, 1.683, 2.0009, 0.05, 1.9606, 0.1557, 1.3765, 1.722, 0.5291, 0.5088, 0.9785, 1.3799]\n",
      "Loss in trainset: 0.3071\n",
      "Loss in testset: 0.3767\n",
      "iteration: 242503\n",
      "w: [1.9164, 2.0446, 2.5342, 1.8921, 1.9071, 0.085, 1.9648, 0.235, 1.4187, 1.6877, 0.4738, 0.5451, 1.4024, 1.3094]\n",
      "iteration: 323399\n",
      "w: [1.9164, 2.0446, 2.1906, 1.8746, 1.6949, 0.0613, 1.9042, 0.1992, 1.3586, 1.6235, 0.4519, 0.5613, 1.1661, 1.3871]\n",
      "Loss in trainset: 0.3036\n",
      "Loss in testset: 0.3723\n",
      "iteration: 404110\n",
      "w: [1.9164, 2.0446, 2.0055, 1.6446, 1.5167, 0.1016, 1.9087, 0.2616, 1.3443, 1.6006, 0.4101, 0.5461, 1.1362, 1.3636]\n",
      "iteration: 485006\n",
      "w: [1.9164, 2.0446, 1.9586, 1.7508, 1.5804, 0.0802, 1.9118, 0.2044, 1.3415, 1.5189, 0.4309, 0.6026, 1.1702, 1.4125]\n",
      "Loss in trainset: 0.3028\n",
      "Loss in testset: 0.3710\n",
      "iteration: 565717\n",
      "w: [1.9164, 2.0446, 2.0167, 1.6061, 1.5748, 0.0957, 1.858, 0.2865, 1.2801, 1.5003, 0.4103, 0.5714, 1.118, 1.4296]\n",
      "iteration: 646613\n",
      "w: [1.9164, 2.0446, 1.9183, 1.6713, 1.5202, 0.0796, 1.8945, 0.226, 1.3058, 1.4683, 0.4114, 0.5599, 1.1146, 1.4307]\n",
      "Loss in trainset: 0.3028\n",
      "Loss in testset: 0.3712\n",
      "iteration: 727324\n",
      "w: [1.9164, 2.0446, 1.9222, 1.6505, 1.5173, 0.0975, 1.882, 0.2366, 1.2903, 1.4728, 0.3992, 0.5819, 1.1226, 1.4406]\n",
      "iteration: 808220\n",
      "w: [1.9164, 2.0446, 1.9205, 1.6534, 1.5172, 0.094, 1.8808, 0.2373, 1.2884, 1.4714, 0.3989, 0.5802, 1.1189, 1.4421]\n",
      "Loss in trainset: 0.3026\n",
      "Loss in testset: 0.3707\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 185232 TEST: 39661\n",
      "dataset built\n",
      "Loss in trainset: 0.3659\n",
      "Loss in testset: 0.3689\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "309a7686e1664469a160175fea73118e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/28730 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.1288, 2.2042, 7.0, 2.0, 2.0, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2aad9aab6e1a4be280b932663dc0918e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/782510 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3529\n",
      "Loss in testset: 0.3566\n",
      "iteration: 78336\n",
      "w: [1.2136, 2.232, 4.1035, 2.8615, 2.3024, 0.0615, 1.9428, 0.1911, 1.3357, 1.8595, 0.4397, 0.4944, 0.8431, 1.1802]\n",
      "iteration: 156672\n",
      "w: [1.2136, 2.232, 2.9407, 2.0874, 2.2832, 0.1102, 1.9373, 0.2248, 1.4065, 1.7909, 0.4922, 0.6382, 1.2594, 1.2579]\n",
      "Loss in trainset: 0.3155\n",
      "Loss in testset: 0.3118\n",
      "iteration: 234838\n",
      "w: [1.2136, 2.232, 2.4839, 1.8619, 2.3964, 0.1153, 1.9584, 0.2258, 1.4409, 1.8016, 0.4279, 0.5882, 1.5316, 1.2504]\n",
      "iteration: 313174\n",
      "w: [1.2136, 2.232, 2.3053, 1.9607, 1.9345, 0.0742, 1.934, 0.2846, 1.4212, 1.8335, 0.3578, 0.6652, 1.4174, 1.3003]\n",
      "Loss in trainset: 0.3169\n",
      "Loss in testset: 0.3128\n",
      "iteration: 391340\n",
      "w: [1.2136, 2.232, 2.2301, 2.0327, 1.8166, 0.1445, 1.9053, 0.2611, 1.4088, 1.6682, 0.4496, 0.563, 1.2377, 1.3423]\n",
      "iteration: 469676\n",
      "w: [1.2136, 2.232, 1.9635, 1.7752, 1.9221, 0.1154, 1.9657, 0.238, 1.4464, 1.6253, 0.4607, 0.54, 1.2183, 1.3571]\n",
      "Loss in trainset: 0.3157\n",
      "Loss in testset: 0.3118\n",
      "iteration: 547842\n",
      "w: [1.2136, 2.232, 2.0066, 1.7995, 1.8222, 0.1182, 1.9322, 0.2607, 1.4134, 1.6757, 0.3852, 0.6071, 1.2613, 1.359]\n",
      "iteration: 626178\n",
      "w: [1.2136, 2.232, 1.9621, 1.8322, 1.815, 0.0841, 1.9298, 0.2495, 1.4033, 1.62, 0.4174, 0.5634, 1.2188, 1.3948]\n",
      "Loss in trainset: 0.3149\n",
      "Loss in testset: 0.3107\n",
      "iteration: 704344\n",
      "w: [1.2136, 2.232, 1.9673, 1.8558, 1.7762, 0.0944, 1.9292, 0.2528, 1.4004, 1.613, 0.4145, 0.58, 1.2065, 1.3921]\n",
      "iteration: 782680\n",
      "w: [1.2136, 2.232, 1.9697, 1.8567, 1.7816, 0.0916, 1.9236, 0.2579, 1.3945, 1.6052, 0.4201, 0.5722, 1.1986, 1.3976]\n",
      "Loss in trainset: 0.3147\n",
      "Loss in testset: 0.3107\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 176384 TEST: 48509\n",
      "dataset built\n",
      "Loss in trainset: 0.3702\n",
      "Loss in testset: 0.3526\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "50d0e6fce8bb42749e20f16408590d60",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/19836 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.0522, 2.0241, 7.0, 2.0, 2.0, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8a5f7f9dce654df8bc212a9499d7fd57",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/782740 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3589\n",
      "Loss in testset: 0.3417\n",
      "iteration: 78336\n",
      "w: [1.0257, 2.1344, 4.006, 2.3757, 2.7782, 0.0908, 1.8775, 0.2236, 1.3176, 1.8924, 0.4354, 0.6454, 0.6475, 1.2375]\n",
      "iteration: 156672\n",
      "w: [1.0257, 2.1344, 2.9202, 1.6642, 1.8449, 0.0709, 1.9829, 0.2238, 1.4383, 1.8618, 0.4101, 0.449, 0.9169, 1.3466]\n",
      "Loss in trainset: 0.3180\n",
      "Loss in testset: 0.3038\n",
      "iteration: 234884\n",
      "w: [1.0257, 2.1344, 2.4117, 1.535, 2.1409, 0.0868, 2.0, 0.1883, 1.5, 1.7253, 0.4266, 0.5278, 1.0596, 1.2984]\n",
      "iteration: 313220\n",
      "w: [1.0257, 2.1344, 2.2603, 1.6636, 1.7575, 0.0844, 1.9091, 0.2578, 1.4116, 1.7727, 0.3665, 0.589, 0.9252, 1.385]\n",
      "Loss in trainset: 0.3172\n",
      "Loss in testset: 0.3033\n",
      "iteration: 391432\n",
      "w: [1.0257, 2.1344, 2.1211, 1.7061, 1.6697, 0.1001, 1.9114, 0.2542, 1.4106, 1.6909, 0.3929, 0.6039, 0.7204, 1.3948]\n",
      "iteration: 469768\n",
      "w: [1.0257, 2.1344, 1.9617, 1.4752, 1.7545, 0.0815, 1.8975, 0.2925, 1.3901, 1.5895, 0.4256, 0.499, 0.7035, 1.4389]\n",
      "Loss in trainset: 0.3190\n",
      "Loss in testset: 0.3043\n",
      "iteration: 547980\n",
      "w: [1.0257, 2.1344, 1.9251, 1.6248, 1.6304, 0.1157, 1.9212, 0.2414, 1.3987, 1.6534, 0.3505, 0.5783, 0.723, 1.4404]\n",
      "iteration: 626316\n",
      "w: [1.0257, 2.1344, 1.89, 1.5499, 1.6325, 0.0859, 1.9203, 0.2528, 1.3937, 1.611, 0.3656, 0.5482, 0.6857, 1.4569]\n",
      "Loss in trainset: 0.3168\n",
      "Loss in testset: 0.3029\n",
      "iteration: 704528\n",
      "w: [1.0257, 2.1344, 1.8741, 1.5312, 1.6149, 0.0863, 1.9191, 0.2525, 1.3893, 1.6246, 0.3467, 0.5575, 0.6819, 1.4616]\n",
      "iteration: 782864\n",
      "w: [1.0257, 2.1344, 1.8755, 1.529, 1.6124, 0.0897, 1.92, 0.2528, 1.3898, 1.6177, 0.3517, 0.5547, 0.6743, 1.4596]\n",
      "Loss in trainset: 0.3167\n",
      "Loss in testset: 0.3029\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TRAIN: 176384 TEST: 48509\n",
      "dataset built\n",
      "Loss in trainset: 0.3612\n",
      "Loss in testset: 0.3855\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "363d8d754305489386387c63accc7515",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "pre-train:   0%|          | 0/28970 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w: [1.1091, 2.0532, 7.0, 2.0, 2.0, 0.2, 1.4, 0.2, 0.8, 2.0, 0.2, 0.2, 0.2, 1.0]\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "80fffbab30f645bb85740f2bb99cd769",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "train:   0%|          | 0/737070 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss in trainset: 0.3489\n",
      "Loss in testset: 0.3756\n",
      "iteration: 73728\n",
      "w: [1.1449, 2.0964, 3.9565, 3.0537, 2.6436, 0.05, 1.9494, 0.199, 1.3784, 1.8597, 0.4444, 0.6256, 1.0515, 1.1609]\n",
      "iteration: 147456\n",
      "w: [1.1449, 2.0964, 3.1128, 1.7751, 1.8642, 0.05, 1.8918, 0.2204, 1.4048, 1.7373, 0.509, 0.4956, 1.0587, 1.3563]\n",
      "Loss in trainset: 0.3167\n",
      "Loss in testset: 0.3298\n",
      "iteration: 221142\n",
      "w: [1.1449, 2.0964, 2.5946, 1.7406, 2.0502, 0.1263, 1.9995, 0.2129, 1.5, 1.6897, 0.4658, 0.4497, 1.1385, 1.3426]\n",
      "iteration: 294870\n",
      "w: [1.1449, 2.0964, 2.2632, 1.5963, 1.9454, 0.1038, 1.9074, 0.246, 1.4176, 1.6572, 0.3951, 0.5904, 1.258, 1.4197]\n",
      "Loss in trainset: 0.3112\n",
      "Loss in testset: 0.3251\n",
      "iteration: 368556\n",
      "w: [1.1449, 2.0964, 2.0624, 1.5608, 1.7545, 0.0872, 1.9118, 0.2276, 1.4118, 1.568, 0.4279, 0.5752, 1.1554, 1.4382]\n",
      "iteration: 442284\n",
      "w: [1.1449, 2.0964, 2.0696, 1.6474, 1.645, 0.0944, 1.8892, 0.2506, 1.3878, 1.4865, 0.4439, 0.5929, 1.0973, 1.472]\n",
      "Loss in trainset: 0.3115\n",
      "Loss in testset: 0.3249\n",
      "iteration: 515970\n",
      "w: [1.1449, 2.0964, 2.0051, 1.5235, 1.5829, 0.0995, 1.8987, 0.252, 1.3906, 1.5185, 0.388, 0.5847, 1.0899, 1.4767]\n",
      "iteration: 589698\n",
      "w: [1.1449, 2.0964, 1.9722, 1.5534, 1.6082, 0.0907, 1.901, 0.2608, 1.3883, 1.4946, 0.3791, 0.5924, 1.0766, 1.4812]\n",
      "Loss in trainset: 0.3110\n",
      "Loss in testset: 0.3247\n",
      "iteration: 663384\n",
      "w: [1.1449, 2.0964, 1.9348, 1.5518, 1.5835, 0.0925, 1.9194, 0.2448, 1.403, 1.4808, 0.3841, 0.5933, 1.0738, 1.4758]\n",
      "iteration: 737112\n",
      "w: [1.1449, 2.0964, 1.9398, 1.5595, 1.5818, 0.096, 1.9162, 0.2456, 1.3994, 1.4823, 0.3808, 0.5985, 1.0729, 1.4792]\n",
      "Loss in trainset: 0.3109\n",
      "Loss in testset: 0.3247\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Training finished!\n"
     ]
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"./revlog_history.tsv\", sep='\\t', index_col=None, dtype={'r_history': str ,'t_history': str} )\n",
    "dataset = dataset[(dataset['i'] > 1) & (dataset['delta_t'] > 0) & (dataset['t_history'].str.count(',0') == 0)]\n",
    "dataset['tensor'] = dataset.progress_apply(lambda x: lineToTensor(list(zip([x['t_history']], [x['r_history']]))[0]), axis=1)\n",
    "dataset['y'] = dataset['r'].map({1: 0, 2: 1, 3: 1, 4: 1})\n",
    "dataset['group'] = dataset['r_history'] + dataset['t_history']\n",
    "print(\"Tensorized!\")\n",
    "\n",
    "from sklearn.model_selection import StratifiedGroupKFold\n",
    "\n",
    "w = []\n",
    "\n",
    "sgkf = StratifiedGroupKFold(n_splits=5)\n",
    "for train_index, test_index in sgkf.split(dataset, dataset['i'], dataset['group']):\n",
    "    print(\"TRAIN:\", len(train_index), \"TEST:\",  len(test_index))\n",
    "    train_set = dataset.iloc[train_index].copy()\n",
    "    test_set = dataset.iloc[test_index].copy()\n",
    "    optimizer = Optimizer(train_set, test_set, n_epoch=5, lr=4e-2, batch_size=512)\n",
    "    w.append(optimizer.train())\n",
    "    optimizer.plot()\n",
    "\n",
    "print(\"\\nTraining finished!\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Result\n",
    "\n",
    "Copy the optimal parameters for FSRS for you in the output of next code cell after running."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.2775, 2.0764, 1.9004, 1.6447, 1.6023, 0.0922, 1.9161, 0.2548, 1.3741, 1.5452, 0.3826, 0.571, 0.9533, 1.4526]\n"
     ]
    }
   ],
   "source": [
    "w = np.array(w)\n",
    "avg_w = np.round(np.mean(w, axis=0), 4)\n",
    "print(avg_w.tolist())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.4 Preview\n",
    "\n",
    "You can see the memory states and intervals generated by FSRS as if you press the good in each review at the due date scheduled by FSRS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1:again, 2:hard, 3:good, 4:easy\n",
      "\n",
      "first rating: 1\n",
      "rating history: 1,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,1d,2d,4d,7d,13d,22d,1.2m,2.0m,3.2m,5.0m\n",
      "difficulty history: 0,5.2,4.9,4.6,4.4,4.1,3.9,3.7,3.6,3.4,3.3\n",
      "\n",
      "first rating: 2\n",
      "rating history: 2,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,3d,7d,15d,29d,1.8m,3.2m,5.4m,8.8m,1.1y,1.8y\n",
      "difficulty history: 0,3.5,3.4,3.3,3.1,3.0,2.9,2.8,2.7,2.7,2.6\n",
      "\n",
      "first rating: 3\n",
      "rating history: 3,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,5d,18d,1.7m,4.2m,9.1m,1.4y,2.6y,4.4y,7.0y,10.8y\n",
      "difficulty history: 0,1.9,1.9,1.9,1.9,1.9,1.9,1.9,1.9,1.9,1.9\n",
      "\n",
      "first rating: 4\n",
      "rating history: 4,3,3,3,3,3,3,3,3,3,3\n",
      "interval history: 0d,8d,1.7m,6.6m,1.6y,3.8y,7.7y,13.9y,23.2y,36.3y,53.9y\n",
      "difficulty history: 0,1.0,1.1,1.2,1.2,1.3,1.3,1.4,1.4,1.5,1.5\n",
      "\n"
     ]
    }
   ],
   "source": [
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "\n",
    "\n",
    "class Collection:\n",
    "    def __init__(self, w):\n",
    "        self.model = FSRS(w)\n",
    "        self.model.eval()\n",
    "\n",
    "    def predict(self, t_history, r_history):\n",
    "        with torch.no_grad():\n",
    "            line_tensor = lineToTensor(list(zip([t_history], [r_history]))[0]).unsqueeze(1)\n",
    "            output_t = self.model(line_tensor)\n",
    "            return output_t[-1][0]\n",
    "        \n",
    "    def batch_predict(self, dataset):\n",
    "        with torch.no_grad():\n",
    "            fast_dataset = RevlogDataset(dataset)\n",
    "            outputs, _ = self.model(fast_dataset.x_train.transpose(0, 1))\n",
    "            stabilities, difficulties = outputs[fast_dataset.seq_len-1, torch.arange(len(fast_dataset))].transpose(0, 1)\n",
    "            return stabilities.tolist(), difficulties.tolist()\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "print(\"1:again, 2:hard, 3:good, 4:easy\\n\")\n",
    "for first_rating in (1,2,3,4):\n",
    "    print(f'first rating: {first_rating}')\n",
    "    t_history = \"0\"\n",
    "    d_history = \"0\"\n",
    "    r_history = f\"{first_rating}\"  # the first rating of the new card\n",
    "    # print(\"stability, difficulty, lapses\")\n",
    "    for i in range(10):\n",
    "        states = my_collection.predict(t_history, r_history)\n",
    "        # print('{0:9.2f} {1:11.2f} {2:7.0f}'.format(\n",
    "            # *list(map(lambda x: round(float(x), 4), states))))\n",
    "        next_t = max(round(float(np.log(requestRetention)/np.log(0.9) * states[0])), 1)\n",
    "        difficulty = round(float(states[1]), 1)\n",
    "        t_history += f',{int(next_t)}'\n",
    "        d_history += f',{difficulty}'\n",
    "        r_history += f\",3\"\n",
    "    print(f\"rating history: {r_history}\")\n",
    "    print(\"interval history: \" + \",\".join([f\"{ivl}d\" if ivl < 30 else f\"{ivl / 30:.1f}m\" if ivl < 365 else f\"{ivl / 365:.1f}y\" for ivl in map(int, t_history.split(','))]))\n",
    "    print(f\"difficulty history: {d_history}\")\n",
    "    print('')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can change the `test_rating_sequence` to see the scheduling intervals in different ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([5.4303, 1.9004])\n",
      "tensor([18.2099,  1.9004])\n",
      "tensor([52.0453,  1.9004])\n",
      "tensor([126.8559,   1.9004])\n",
      "tensor([272.4645,   1.9004])\n",
      "tensor([22.9038,  4.8095])\n",
      "tensor([4.7137, 7.4504])\n",
      "tensor([6.7371, 6.9387])\n",
      "tensor([9.5965, 6.4742])\n",
      "tensor([13.7131,  6.0525])\n",
      "tensor([19.4975,  5.6697])\n",
      "tensor([27.3595,  5.3221])\n",
      "rating history: 3,3,3,3,3,1,1,3,3,3,3,3\n",
      "interval history: 0,5,18,52,127,272,23,5,7,10,14,19,27\n",
      "difficulty history: 0,1.9,1.9,1.9,1.9,1.9,4.8,7.5,6.9,6.5,6.1,5.7,5.3\n"
     ]
    }
   ],
   "source": [
    "test_rating_sequence = \"3,3,3,3,3,1,1,3,3,3,3,3\"\n",
    "requestRetention = 0.9  # recommended setting: 0.8 ~ 0.9\n",
    "easyBonus = 1.3\n",
    "hardInterval = 1.2\n",
    "\n",
    "t_history = \"0\"\n",
    "d_history = \"0\"\n",
    "for i in range(len(test_rating_sequence.split(','))):\n",
    "    rating = test_rating_sequence[2*i]\n",
    "    last_t = int(t_history.split(',')[-1])\n",
    "    r_history = test_rating_sequence[:2*i+1]\n",
    "    states = my_collection.predict(t_history, r_history)\n",
    "    print(states)\n",
    "    next_t = max(1,round(float(np.log(requestRetention)/np.log(0.9) * states[0])))\n",
    "    if rating == '4':\n",
    "        next_t = round(next_t * easyBonus)\n",
    "    elif rating == '2':\n",
    "        next_t = round(last_t * hardInterval)\n",
    "    t_history += f',{int(next_t)}'\n",
    "    difficulty = round(float(states[1]), 1)\n",
    "    d_history += f',{difficulty}'\n",
    "print(f\"rating history: {test_rating_sequence}\")\n",
    "print(f\"interval history: {t_history}\")\n",
    "print(f\"difficulty history: {d_history}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.5 Predict memory states and distribution of difficulty\n",
    "\n",
    "Predict memory states for each review group and save them in [./prediction.tsv](./prediction.tsv).\n",
    "\n",
    "Meanwhile, it will count the distribution of difficulty."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prediction.tsv saved.\n",
      "difficulty\n",
      "1     0.195933\n",
      "2     0.141343\n",
      "3     0.068055\n",
      "4     0.068312\n",
      "5     0.126536\n",
      "6     0.078326\n",
      "7     0.077713\n",
      "8     0.074609\n",
      "9     0.054973\n",
      "10    0.042385\n",
      "11    0.028004\n",
      "12    0.019138\n",
      "13    0.011810\n",
      "14    0.007604\n",
      "15    0.005260\n",
      "Name: count, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "stabilities = map(lambda x: round(x, 2), stabilities)\n",
    "difficulties = map(lambda x: round(x, 2), difficulties)\n",
    "dataset['stability'] = list(stabilities)\n",
    "dataset['difficulty'] = list(difficulties)\n",
    "prediction = dataset.groupby(by=['t_history', 'r_history']).agg({\"stability\": \"mean\", \"difficulty\": \"mean\", \"id\": \"count\"})\n",
    "prediction.reset_index(inplace=True)\n",
    "prediction.sort_values(by=['r_history'], inplace=True)\n",
    "prediction.rename(columns={\"id\": \"count\"}, inplace=True)\n",
    "prediction.to_csv(\"./prediction.tsv\", sep='\\t', index=None)\n",
    "print(\"prediction.tsv saved.\")\n",
    "prediction['difficulty'] = prediction['difficulty'].map(lambda x: int(round(x)))\n",
    "difficulty_distribution = prediction.groupby(by=['difficulty'])['count'].sum() / prediction['count'].sum()\n",
    "print(difficulty_distribution)\n",
    "difficulty_distribution_padding = np.zeros(15)\n",
    "for i in range(15):\n",
    "    if i+1 in difficulty_distribution.index:\n",
    "        difficulty_distribution_padding[i] = difficulty_distribution.loc[i+1]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Optimize retention to minimize the time of reviews\n",
    "\n",
    "Calculate the optimal retention to minimize the time for long-term memory consolidation. It is an experimental feature. You can use the simulator to get more accurate results:\n",
    "\n",
    "https://github.com/open-spaced-repetition/fsrs4anki/blob/main/fsrs4anki_simulator.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average time for failed cards: 25.0s\n",
      "average time for recalled cards: 8.0s\n",
      "terminal stability:  100.18\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "52556a8a0b154398a3df9a12c025cb3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/15 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "expected_time.csv saved.\n",
      "\n",
      "-----suggested retention (experimental): 0.83-----\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "base = 1.01\n",
    "index_len = 664\n",
    "index_offset = 200\n",
    "d_range = 15\n",
    "d_offset = 1\n",
    "r_time = 8\n",
    "f_time = 25\n",
    "max_time = 200000\n",
    "\n",
    "type_block = dict()\n",
    "type_count = dict()\n",
    "type_time = dict()\n",
    "last_t = type_sequence[0]\n",
    "type_block[last_t] = 1\n",
    "type_count[last_t] = 1\n",
    "type_time[last_t] = time_sequence[0]\n",
    "for i,t in enumerate(type_sequence[1:]):\n",
    "    type_count[t] = type_count.setdefault(t, 0) + 1\n",
    "    type_time[t] = type_time.setdefault(t, 0) + time_sequence[i]\n",
    "    if t != last_t:\n",
    "        type_block[t] = type_block.setdefault(t, 0) + 1\n",
    "    last_t = t\n",
    "\n",
    "r_time = round(type_time[1]/type_count[1]/1000, 1)\n",
    "\n",
    "if 2 in type_count and 2 in type_block:\n",
    "    f_time = round(type_time[2]/type_block[2]/1000 + r_time, 1)\n",
    "\n",
    "print(f\"average time for failed cards: {f_time}s\")\n",
    "print(f\"average time for recalled cards: {r_time}s\")\n",
    "\n",
    "def stability2index(stability):\n",
    "    return int(round(np.log(stability) / np.log(base)) + index_offset)\n",
    "\n",
    "def init_stability(d):\n",
    "    return max(((d - avg_w[2]) / -avg_w[3] + 2) * avg_w[1] + avg_w[0], np.power(base, -index_offset))\n",
    "\n",
    "def cal_next_recall_stability(s, r, d, response):\n",
    "    if response == 1:\n",
    "        return s * (1 + np.exp(avg_w[6]) * 10 * np.power(d, -avg_w[13]) * np.power(s, -avg_w[7]) * (np.exp((1 - r) * avg_w[8]) - 1))\n",
    "    else:\n",
    "        return avg_w[9] * np.power(d, -avg_w[10]) * np.power(s, avg_w[11]) * np.exp((1 - r) * avg_w[12])\n",
    "\n",
    "\n",
    "stability_list = np.array([np.power(base, i - index_offset) for i in range(index_len)])\n",
    "print(f\"terminal stability: {stability_list.max(): .2f}\")\n",
    "df = pd.DataFrame(columns=[\"retention\", \"difficulty\", \"time\"])\n",
    "\n",
    "for percentage in notebook.tqdm(range(96, 66, -2)):\n",
    "    recall = percentage / 100\n",
    "    time_list = np.zeros((d_range, index_len))\n",
    "    time_list[:,:-1] = max_time\n",
    "    for d in range(d_range, 0, -1):\n",
    "        s0 = init_stability(d)\n",
    "        s0_index = stability2index(s0)\n",
    "        diff = max_time\n",
    "        while diff > 0.1:\n",
    "            s0_time = time_list[d - 1][s0_index]\n",
    "            for s_index in range(index_len - 2, -1, -1):\n",
    "                stability = stability_list[s_index]\n",
    "                interval = max(1, round(stability * np.log(recall) / np.log(0.9)))\n",
    "                p_recall = np.power(0.9, interval / stability)\n",
    "                recall_s = cal_next_recall_stability(stability, p_recall, d, 1)\n",
    "                forget_d = min(d + d_offset, 15)\n",
    "                forget_s = cal_next_recall_stability(stability, p_recall, forget_d, 0)\n",
    "                recall_s_index = min(stability2index(recall_s), index_len - 1)\n",
    "                forget_s_index = min(max(stability2index(forget_s), 0), index_len - 1)\n",
    "                recall_time = time_list[d - 1][recall_s_index] + r_time\n",
    "                forget_time = time_list[forget_d - 1][forget_s_index] + f_time\n",
    "                exp_time = p_recall * recall_time + (1.0 - p_recall) * forget_time\n",
    "                if exp_time < time_list[d - 1][s_index]:\n",
    "                    time_list[d - 1][s_index] = exp_time\n",
    "            diff = s0_time - time_list[d - 1][s0_index]\n",
    "        df.loc[0 if pd.isnull(df.index.max()) else df.index.max() + 1] = [recall, d, s0_time]\n",
    "\n",
    "df.sort_values(by=[\"difficulty\", \"retention\"], inplace=True)\n",
    "df.to_csv(\"./expected_time.csv\", index=False)\n",
    "print(\"expected_time.csv saved.\")\n",
    "\n",
    "optimal_retention_list = np.zeros(15)\n",
    "for d in range(1, d_range+1):\n",
    "    retention = df[df[\"difficulty\"] == d][\"retention\"]\n",
    "    time = df[df[\"difficulty\"] == d][\"time\"]\n",
    "    optimal_retention = retention.iat[time.argmin()]\n",
    "    optimal_retention_list[d-1] = optimal_retention\n",
    "    plt.plot(retention, time, label=f\"d={d}, r={optimal_retention}\")\n",
    "print(f\"\\n-----suggested retention (experimental): {np.inner(difficulty_distribution_padding, optimal_retention_list):.2f}-----\")\n",
    "plt.ylabel(\"expected time (second)\")\n",
    "plt.xlabel(\"retention\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.semilogy()\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 Evaluate the model"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.1 Loss\n",
    "\n",
    "Evaluate the model with the log loss. It will compare the log loss between initial model and trained model.\n",
    "\n",
    "And it will predict the stability, difficulty and retrievability for each revlog in [./evaluation.tsv](./evaluation.tsv)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loss before training: 0.3664\n",
      "Loss after training: 0.3140\n"
     ]
    }
   ],
   "source": [
    "my_collection = Collection(init_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss before training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "my_collection = Collection(avg_w)\n",
    "stabilities, difficulties = my_collection.batch_predict(dataset)\n",
    "dataset['stability'] = stabilities\n",
    "dataset['difficulty'] = difficulties\n",
    "dataset['p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['stability'])\n",
    "dataset['log_loss'] = dataset.apply(lambda row: - np.log(row['p']) if row['y'] == 1 else - np.log(1 - row['p']), axis=1)\n",
    "print(f\"Loss after training: {dataset['log_loss'].mean():.4f}\")\n",
    "\n",
    "tmp = dataset.copy()\n",
    "tmp['stability'] = tmp['stability'].map(lambda x: round(x, 2))\n",
    "tmp['difficulty'] = tmp['difficulty'].map(lambda x: round(x, 2))\n",
    "tmp['p'] = tmp['p'].map(lambda x: round(x, 2))\n",
    "tmp['log_loss'] = tmp['log_loss'].map(lambda x: round(x, 2))\n",
    "tmp.rename(columns={\"r\": \"grade\", \"p\": \"retrievability\"}, inplace=True)\n",
    "tmp[['id', 'cid', 'review_date', 'r_history', 't_history', 'delta_t', 'grade', 'stability', 'difficulty', 'retrievability', 'log_loss']].to_csv(\"./evaluation.tsv\", sep='\\t', index=False)\n",
    "del tmp"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.2 Calibration graph\n",
    "\n",
    "1. FSRS predicts the stability and retention for each review.\n",
    "2. Reviews are grouped into 40 bins according to their predicted retention.\n",
    "3. Count the true retention of each bin.\n",
    "4. Plot (predicted retention, true retention) in the line graph.\n",
    "5. Plot (predicted retention, size of bin) in the bar graph.\n",
    "6. Combine these graphs to create the calibration graph.\n",
    "\n",
    "Ideally, the blue line should be aligned with the orange one. If the blue line is higher than the orange line, the FSRS underestimates the retention. When the size of reviews within a bin is small, actual retention may deviate largely, which is normal.\n",
    "\n",
    "R-squared (aka the coefficient of determination), is the proportion of the variation in the dependent variable that is predictable from the independent variable(s). The higher the R-squared, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Coefficient_of_determination\n",
    "\n",
    "RMSE (root mean squared error) is the square root of the average of squared differences between prediction and actual observation. The lower the RMSE, the better the model fits your data. For details, please see https://en.wikipedia.org/wiki/Root-mean-square_deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R-squared: 0.9177\n",
      "RMSE: 0.0189\n",
      "[0.14543872 0.835701  ]\n",
      "Last rating: 1\n",
      "R-squared: 0.2348\n",
      "RMSE: 0.0626\n",
      "[0.28532467 0.64010612]\n",
      "Last rating: 2\n",
      "R-squared: 0.4391\n",
      "RMSE: 0.0436\n",
      "[-0.19677131  1.17157839]\n",
      "Last rating: 3\n",
      "R-squared: 0.9276\n",
      "RMSE: 0.0195\n",
      "[0.09888907 0.90170716]\n",
      "Last rating: 4\n",
      "R-squared: 0.5585\n",
      "RMSE: 0.0295\n",
      "[0.30109566 0.69104271]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "# code from https://github.com/papousek/duolingo-halflife-regression/blob/master/evaluation.py\n",
    "def load_brier(predictions, real, bins=20):\n",
    "    counts = np.zeros(bins)\n",
    "    correct = np.zeros(bins)\n",
    "    prediction = np.zeros(bins)\n",
    "    for p, r in zip(predictions, real):\n",
    "        bin = min(int(p * bins), bins - 1)\n",
    "        counts[bin] += 1\n",
    "        correct[bin] += r\n",
    "        prediction[bin] += p\n",
    "    np.seterr(invalid='ignore')\n",
    "    prediction_means = prediction / counts\n",
    "    prediction_means[np.isnan(prediction_means)] = ((np.arange(bins) + 0.5) / bins)[np.isnan(prediction_means)]\n",
    "    correct_means = correct / counts\n",
    "    correct_means[np.isnan(correct_means)] = 0\n",
    "    size = len(predictions)\n",
    "    answer_mean = sum(correct) / size\n",
    "    return {\n",
    "        \"reliability\": sum(counts * (correct_means - prediction_means) ** 2) / size,\n",
    "        \"resolution\": sum(counts * (correct_means - answer_mean) ** 2) / size,\n",
    "        \"uncertainty\": answer_mean * (1 - answer_mean),\n",
    "        \"detail\": {\n",
    "            \"bin_count\": bins,\n",
    "            \"bin_counts\": list(counts),\n",
    "            \"bin_prediction_means\": list(prediction_means),\n",
    "            \"bin_correct_means\": list(correct_means),\n",
    "        }\n",
    "    }\n",
    "\n",
    "\n",
    "def plot_brier(predictions, real, bins=20):\n",
    "    brier = load_brier(predictions, real, bins=bins)\n",
    "    bin_prediction_means = brier['detail']['bin_prediction_means']\n",
    "    bin_correct_means = brier['detail']['bin_correct_means']\n",
    "    bin_counts = brier['detail']['bin_counts']\n",
    "    r2 = r2_score(bin_correct_means, bin_prediction_means, sample_weight=bin_counts)\n",
    "    rmse = mean_squared_error(bin_correct_means, bin_prediction_means, sample_weight=bin_counts, squared=False)\n",
    "    print(f\"R-squared: {r2:.4f}\")\n",
    "    print(f\"RMSE: {rmse:.4f}\")\n",
    "    plt.figure()\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, 1])\n",
    "    ax.set_ylim([0, 1])\n",
    "    plt.grid(True)\n",
    "    fit_wls = sm.WLS(bin_correct_means, sm.add_constant(bin_prediction_means), weights=bin_counts).fit()\n",
    "    print(fit_wls.params)\n",
    "    y_regression = [fit_wls.params[0] + fit_wls.params[1]*x for x in bin_prediction_means]\n",
    "    plt.plot(bin_prediction_means, y_regression, label='Weighted Least Squares Regression', color=\"green\")\n",
    "    plt.plot(bin_prediction_means, bin_correct_means, label='Actual Calibration', color=\"#1f77b4\")\n",
    "    plt.plot((0, 1), (0, 1), label='Perfect Calibration', color=\"#ff7f0e\")\n",
    "    bin_count = brier['detail']['bin_count']\n",
    "    counts = np.array(bin_counts)\n",
    "    bins = (np.arange(bin_count) + 0.5) / bin_count\n",
    "    plt.legend(loc='upper center')\n",
    "    plt.xlabel('Predicted R')\n",
    "    plt.ylabel('Actual R')\n",
    "    plt.twinx()\n",
    "    plt.ylabel('Number of reviews')\n",
    "    plt.bar(bins, counts, width=(0.8 / bin_count), ec='k', lw=.2, alpha=0.5, label='Number of reviews')\n",
    "    plt.legend(loc='lower center')\n",
    "\n",
    "\n",
    "plot_brier(dataset['p'], dataset['y'], bins=40)\n",
    "for last_rating in (\"1\",\"2\",\"3\",\"4\"):\n",
    "    print(f\"Last rating: {last_rating}\")\n",
    "    plot_brier(dataset[dataset['r_history'].str.endswith(last_rating)]['p'], dataset[dataset['r_history'].str.endswith(last_rating)]['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as ticker\n",
    "\n",
    "def to_percent(temp, position):\n",
    "    return '%1.0f' % (100 * temp) + '%'\n",
    "\n",
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "stability_calibration = pd.DataFrame(columns=['stability', 'predicted_retention', 'actual_retention'])\n",
    "stability_calibration = dataset[['stability', 'p', 'y']].copy()\n",
    "stability_calibration['bin'] = stability_calibration['stability'].map(lambda x: math.pow(1.2, math.floor(math.log(x, 1.2))))\n",
    "stability_group = stability_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=stability_group.index, height=stability_group['y'], width=stability_group.index / 5.5,\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Stability (days)\")\n",
    "ax1.semilogx()\n",
    "lns.append(lns1)\n",
    "\n",
    "stability_group = stability_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(stability_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(stability_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1)\n",
    "ax1 = fig.add_subplot(111)\n",
    "ax2 = ax1.twinx()\n",
    "lns = []\n",
    "\n",
    "difficulty_calibration = pd.DataFrame(columns=['difficulty', 'predicted_retention', 'actual_retention'])\n",
    "difficulty_calibration = dataset[['difficulty', 'p', 'y']].copy()\n",
    "difficulty_calibration['bin'] = difficulty_calibration['difficulty'].map(round)\n",
    "difficulty_group = difficulty_calibration.groupby('bin').count()\n",
    "\n",
    "lns1 = ax1.bar(x=difficulty_group.index, height=difficulty_group['y'],\n",
    "                ec='k', lw=.2, label='Number of predictions', alpha=0.5)\n",
    "ax1.set_ylabel(\"Number of predictions\")\n",
    "ax1.set_xlabel(\"Difficulty\")\n",
    "lns.append(lns1)\n",
    "\n",
    "difficulty_group = difficulty_calibration.groupby(by='bin').agg('mean')\n",
    "lns2 = ax2.plot(difficulty_group['y'], label='Actual retention')\n",
    "lns3 = ax2.plot(difficulty_group['p'], label='Predicted retention')\n",
    "ax2.set_ylabel(\"Retention\")\n",
    "ax2.set_ylim(0, 1)\n",
    "lns.append(lns2[0])\n",
    "lns.append(lns3[0])\n",
    "\n",
    "labs = [l.get_label() for l in lns]\n",
    "ax2.legend(lns, labs, loc='lower right')\n",
    "plt.grid(linestyle='--')\n",
    "plt.gca().yaxis.set_major_formatter(ticker.FuncFormatter(to_percent))\n",
    "plt.gca().xaxis.set_major_formatter(ticker.FormatStrFormatter('%d'))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  color: #f1f1f1;\n",
       "}\n",
       "#T_397eb_row2_col6, #T_397eb_row5_col5, #T_397eb_row8_col2, #T_397eb_row12_col1 {\n",
       "  background-color: #ffe9e9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row2_col7 {\n",
       "  background-color: #ffd5d5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row2_col8, #T_397eb_row7_col4, #T_397eb_row11_col3, #T_397eb_row12_col5, #T_397eb_row13_col3, #T_397eb_row18_col0 {\n",
       "  background-color: #d9d9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row2_col9 {\n",
       "  background-color: #c5c5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row2_col10, #T_397eb_row6_col0, #T_397eb_row6_col4, #T_397eb_row8_col1, #T_397eb_row12_col3, #T_397eb_row14_col3 {\n",
       "  background-color: #cdcdff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row2_col11 {\n",
       "  background-color: #e5e5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row2_col12, #T_397eb_row3_col11, #T_397eb_row8_col8, #T_397eb_row10_col5, #T_397eb_row11_col6, #T_397eb_row14_col0, #T_397eb_row16_col2 {\n",
       "  background-color: #ffe1e1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row3_col4, #T_397eb_row6_col8, #T_397eb_row8_col3, #T_397eb_row10_col3, #T_397eb_row13_col1 {\n",
       "  background-color: #fff9f9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row3_col6, #T_397eb_row13_col4 {\n",
       "  background-color: #d5d5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row3_col7, #T_397eb_row3_col9, #T_397eb_row5_col6, #T_397eb_row16_col0 {\n",
       "  background-color: #f1f1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row3_col8 {\n",
       "  background-color: #a9a9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row3_col10, #T_397eb_row4_col6, #T_397eb_row6_col5, #T_397eb_row9_col1 {\n",
       "  background-color: #e1e1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row3_col12 {\n",
       "  background-color: #ffc1c1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row4_col3, #T_397eb_row7_col9 {\n",
       "  background-color: #ff9595;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row4_col8, #T_397eb_row9_col4, #T_397eb_row10_col4, #T_397eb_row10_col7, #T_397eb_row11_col4 {\n",
       "  background-color: #e9e9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row4_col9, #T_397eb_row7_col1 {\n",
       "  background-color: #ddddff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row4_col10, #T_397eb_row6_col1, #T_397eb_row9_col0, #T_397eb_row10_col1, #T_397eb_row10_col6, #T_397eb_row12_col0 {\n",
       "  background-color: #fffdfd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row4_col11 {\n",
       "  background-color: #ff7d7d;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_397eb_row5_col4 {\n",
       "  background-color: #a5a5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row5_col7, #T_397eb_row6_col6 {\n",
       "  background-color: #f9f9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row5_col8, #T_397eb_row5_col9, #T_397eb_row7_col6, #T_397eb_row9_col8 {\n",
       "  background-color: #ffeded;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row5_col10, #T_397eb_row6_col10 {\n",
       "  background-color: #ff9191;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row5_col11 {\n",
       "  background-color: #ff5555;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_397eb_row6_col2, #T_397eb_row15_col3 {\n",
       "  background-color: #ffcdcd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row6_col7, #T_397eb_row8_col5, #T_397eb_row8_col6, #T_397eb_row9_col2, #T_397eb_row9_col3, #T_397eb_row9_col6 {\n",
       "  background-color: #fff5f5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row7_col8, #T_397eb_row11_col2, #T_397eb_row11_col5, #T_397eb_row14_col2 {\n",
       "  background-color: #ffc9c9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row9_col5, #T_397eb_row9_col7 {\n",
       "  background-color: #ffd1d1;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row10_col2 {\n",
       "  background-color: #ffe5e5;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row12_col4 {\n",
       "  background-color: #b5b5ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row13_col2 {\n",
       "  background-color: #ffbdbd;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row15_col1 {\n",
       "  background-color: #b1b1ff;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row15_col2 {\n",
       "  background-color: #ffd9d9;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_397eb_row16_col1 {\n",
       "  background-color: #9191ff;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_397eb_row17_col0 {\n",
       "  background-color: #b9b9ff;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_397eb\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >d_bin</th>\n",
       "      <th id=\"T_397eb_level0_col0\" class=\"col_heading level0 col0\" >1</th>\n",
       "      <th id=\"T_397eb_level0_col1\" class=\"col_heading level0 col1\" >2</th>\n",
       "      <th id=\"T_397eb_level0_col2\" class=\"col_heading level0 col2\" >3</th>\n",
       "      <th id=\"T_397eb_level0_col3\" class=\"col_heading level0 col3\" >4</th>\n",
       "      <th id=\"T_397eb_level0_col4\" class=\"col_heading level0 col4\" >5</th>\n",
       "      <th id=\"T_397eb_level0_col5\" class=\"col_heading level0 col5\" >6</th>\n",
       "      <th id=\"T_397eb_level0_col6\" class=\"col_heading level0 col6\" >7</th>\n",
       "      <th id=\"T_397eb_level0_col7\" class=\"col_heading level0 col7\" >8</th>\n",
       "      <th id=\"T_397eb_level0_col8\" class=\"col_heading level0 col8\" >9</th>\n",
       "      <th id=\"T_397eb_level0_col9\" class=\"col_heading level0 col9\" >10</th>\n",
       "      <th id=\"T_397eb_level0_col10\" class=\"col_heading level0 col10\" >11</th>\n",
       "      <th id=\"T_397eb_level0_col11\" class=\"col_heading level0 col11\" >12</th>\n",
       "      <th id=\"T_397eb_level0_col12\" class=\"col_heading level0 col12\" >13</th>\n",
       "      <th id=\"T_397eb_level0_col13\" class=\"col_heading level0 col13\" >14</th>\n",
       "      <th id=\"T_397eb_level0_col14\" class=\"col_heading level0 col14\" >15</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th class=\"index_name level0\" >s_bin</th>\n",
       "      <th class=\"blank col0\" >&nbsp;</th>\n",
       "      <th class=\"blank col1\" >&nbsp;</th>\n",
       "      <th class=\"blank col2\" >&nbsp;</th>\n",
       "      <th class=\"blank col3\" >&nbsp;</th>\n",
       "      <th class=\"blank col4\" >&nbsp;</th>\n",
       "      <th class=\"blank col5\" >&nbsp;</th>\n",
       "      <th class=\"blank col6\" >&nbsp;</th>\n",
       "      <th class=\"blank col7\" >&nbsp;</th>\n",
       "      <th class=\"blank col8\" >&nbsp;</th>\n",
       "      <th class=\"blank col9\" >&nbsp;</th>\n",
       "      <th class=\"blank col10\" >&nbsp;</th>\n",
       "      <th class=\"blank col11\" >&nbsp;</th>\n",
       "      <th class=\"blank col12\" >&nbsp;</th>\n",
       "      <th class=\"blank col13\" >&nbsp;</th>\n",
       "      <th class=\"blank col14\" >&nbsp;</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row0\" class=\"row_heading level0 row0\" >0.710000</th>\n",
       "      <td id=\"T_397eb_row0_col0\" class=\"data row0 col0\" ></td>\n",
       "      <td id=\"T_397eb_row0_col1\" class=\"data row0 col1\" ></td>\n",
       "      <td id=\"T_397eb_row0_col2\" class=\"data row0 col2\" ></td>\n",
       "      <td id=\"T_397eb_row0_col3\" class=\"data row0 col3\" ></td>\n",
       "      <td id=\"T_397eb_row0_col4\" class=\"data row0 col4\" ></td>\n",
       "      <td id=\"T_397eb_row0_col5\" class=\"data row0 col5\" ></td>\n",
       "      <td id=\"T_397eb_row0_col6\" class=\"data row0 col6\" ></td>\n",
       "      <td id=\"T_397eb_row0_col7\" class=\"data row0 col7\" >8.29%</td>\n",
       "      <td id=\"T_397eb_row0_col8\" class=\"data row0 col8\" ></td>\n",
       "      <td id=\"T_397eb_row0_col9\" class=\"data row0 col9\" ></td>\n",
       "      <td id=\"T_397eb_row0_col10\" class=\"data row0 col10\" ></td>\n",
       "      <td id=\"T_397eb_row0_col11\" class=\"data row0 col11\" ></td>\n",
       "      <td id=\"T_397eb_row0_col12\" class=\"data row0 col12\" >-1.75%</td>\n",
       "      <td id=\"T_397eb_row0_col13\" class=\"data row0 col13\" >-0.15%</td>\n",
       "      <td id=\"T_397eb_row0_col14\" class=\"data row0 col14\" >4.85%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row1\" class=\"row_heading level0 row1\" >1.000000</th>\n",
       "      <td id=\"T_397eb_row1_col0\" class=\"data row1 col0\" ></td>\n",
       "      <td id=\"T_397eb_row1_col1\" class=\"data row1 col1\" ></td>\n",
       "      <td id=\"T_397eb_row1_col2\" class=\"data row1 col2\" ></td>\n",
       "      <td id=\"T_397eb_row1_col3\" class=\"data row1 col3\" ></td>\n",
       "      <td id=\"T_397eb_row1_col4\" class=\"data row1 col4\" >5.50%</td>\n",
       "      <td id=\"T_397eb_row1_col5\" class=\"data row1 col5\" ></td>\n",
       "      <td id=\"T_397eb_row1_col6\" class=\"data row1 col6\" ></td>\n",
       "      <td id=\"T_397eb_row1_col7\" class=\"data row1 col7\" >2.86%</td>\n",
       "      <td id=\"T_397eb_row1_col8\" class=\"data row1 col8\" >8.35%</td>\n",
       "      <td id=\"T_397eb_row1_col9\" class=\"data row1 col9\" >-0.63%</td>\n",
       "      <td id=\"T_397eb_row1_col10\" class=\"data row1 col10\" >0.51%</td>\n",
       "      <td id=\"T_397eb_row1_col11\" class=\"data row1 col11\" >-2.47%</td>\n",
       "      <td id=\"T_397eb_row1_col12\" class=\"data row1 col12\" >-0.44%</td>\n",
       "      <td id=\"T_397eb_row1_col13\" class=\"data row1 col13\" >1.40%</td>\n",
       "      <td id=\"T_397eb_row1_col14\" class=\"data row1 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row2\" class=\"row_heading level0 row2\" >1.400000</th>\n",
       "      <td id=\"T_397eb_row2_col0\" class=\"data row2 col0\" ></td>\n",
       "      <td id=\"T_397eb_row2_col1\" class=\"data row2 col1\" ></td>\n",
       "      <td id=\"T_397eb_row2_col2\" class=\"data row2 col2\" ></td>\n",
       "      <td id=\"T_397eb_row2_col3\" class=\"data row2 col3\" ></td>\n",
       "      <td id=\"T_397eb_row2_col4\" class=\"data row2 col4\" ></td>\n",
       "      <td id=\"T_397eb_row2_col5\" class=\"data row2 col5\" >8.63%</td>\n",
       "      <td id=\"T_397eb_row2_col6\" class=\"data row2 col6\" >0.86%</td>\n",
       "      <td id=\"T_397eb_row2_col7\" class=\"data row2 col7\" >1.59%</td>\n",
       "      <td id=\"T_397eb_row2_col8\" class=\"data row2 col8\" >-1.45%</td>\n",
       "      <td id=\"T_397eb_row2_col9\" class=\"data row2 col9\" >-2.25%</td>\n",
       "      <td id=\"T_397eb_row2_col10\" class=\"data row2 col10\" >-1.95%</td>\n",
       "      <td id=\"T_397eb_row2_col11\" class=\"data row2 col11\" >-1.02%</td>\n",
       "      <td id=\"T_397eb_row2_col12\" class=\"data row2 col12\" >1.17%</td>\n",
       "      <td id=\"T_397eb_row2_col13\" class=\"data row2 col13\" ></td>\n",
       "      <td id=\"T_397eb_row2_col14\" class=\"data row2 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row3\" class=\"row_heading level0 row3\" >1.960000</th>\n",
       "      <td id=\"T_397eb_row3_col0\" class=\"data row3 col0\" ></td>\n",
       "      <td id=\"T_397eb_row3_col1\" class=\"data row3 col1\" ></td>\n",
       "      <td id=\"T_397eb_row3_col2\" class=\"data row3 col2\" ></td>\n",
       "      <td id=\"T_397eb_row3_col3\" class=\"data row3 col3\" ></td>\n",
       "      <td id=\"T_397eb_row3_col4\" class=\"data row3 col4\" >0.25%</td>\n",
       "      <td id=\"T_397eb_row3_col5\" class=\"data row3 col5\" ></td>\n",
       "      <td id=\"T_397eb_row3_col6\" class=\"data row3 col6\" >-1.71%</td>\n",
       "      <td id=\"T_397eb_row3_col7\" class=\"data row3 col7\" >-0.57%</td>\n",
       "      <td id=\"T_397eb_row3_col8\" class=\"data row3 col8\" >-3.43%</td>\n",
       "      <td id=\"T_397eb_row3_col9\" class=\"data row3 col9\" >-0.56%</td>\n",
       "      <td id=\"T_397eb_row3_col10\" class=\"data row3 col10\" >-1.24%</td>\n",
       "      <td id=\"T_397eb_row3_col11\" class=\"data row3 col11\" >1.17%</td>\n",
       "      <td id=\"T_397eb_row3_col12\" class=\"data row3 col12\" >2.44%</td>\n",
       "      <td id=\"T_397eb_row3_col13\" class=\"data row3 col13\" ></td>\n",
       "      <td id=\"T_397eb_row3_col14\" class=\"data row3 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row4\" class=\"row_heading level0 row4\" >2.740000</th>\n",
       "      <td id=\"T_397eb_row4_col0\" class=\"data row4 col0\" ></td>\n",
       "      <td id=\"T_397eb_row4_col1\" class=\"data row4 col1\" ></td>\n",
       "      <td id=\"T_397eb_row4_col2\" class=\"data row4 col2\" ></td>\n",
       "      <td id=\"T_397eb_row4_col3\" class=\"data row4 col3\" >4.12%</td>\n",
       "      <td id=\"T_397eb_row4_col4\" class=\"data row4 col4\" >-0.12%</td>\n",
       "      <td id=\"T_397eb_row4_col5\" class=\"data row4 col5\" >1.30%</td>\n",
       "      <td id=\"T_397eb_row4_col6\" class=\"data row4 col6\" >-1.23%</td>\n",
       "      <td id=\"T_397eb_row4_col7\" class=\"data row4 col7\" >-1.83%</td>\n",
       "      <td id=\"T_397eb_row4_col8\" class=\"data row4 col8\" >-0.78%</td>\n",
       "      <td id=\"T_397eb_row4_col9\" class=\"data row4 col9\" >-1.26%</td>\n",
       "      <td id=\"T_397eb_row4_col10\" class=\"data row4 col10\" >0.05%</td>\n",
       "      <td id=\"T_397eb_row4_col11\" class=\"data row4 col11\" >5.12%</td>\n",
       "      <td id=\"T_397eb_row4_col12\" class=\"data row4 col12\" ></td>\n",
       "      <td id=\"T_397eb_row4_col13\" class=\"data row4 col13\" ></td>\n",
       "      <td id=\"T_397eb_row4_col14\" class=\"data row4 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row5\" class=\"row_heading level0 row5\" >3.840000</th>\n",
       "      <td id=\"T_397eb_row5_col0\" class=\"data row5 col0\" ></td>\n",
       "      <td id=\"T_397eb_row5_col1\" class=\"data row5 col1\" ></td>\n",
       "      <td id=\"T_397eb_row5_col2\" class=\"data row5 col2\" ></td>\n",
       "      <td id=\"T_397eb_row5_col3\" class=\"data row5 col3\" >-0.36%</td>\n",
       "      <td id=\"T_397eb_row5_col4\" class=\"data row5 col4\" >-3.48%</td>\n",
       "      <td id=\"T_397eb_row5_col5\" class=\"data row5 col5\" >0.82%</td>\n",
       "      <td id=\"T_397eb_row5_col6\" class=\"data row5 col6\" >-0.50%</td>\n",
       "      <td id=\"T_397eb_row5_col7\" class=\"data row5 col7\" >-0.16%</td>\n",
       "      <td id=\"T_397eb_row5_col8\" class=\"data row5 col8\" >0.65%</td>\n",
       "      <td id=\"T_397eb_row5_col9\" class=\"data row5 col9\" >0.73%</td>\n",
       "      <td id=\"T_397eb_row5_col10\" class=\"data row5 col10\" >4.24%</td>\n",
       "      <td id=\"T_397eb_row5_col11\" class=\"data row5 col11\" >6.72%</td>\n",
       "      <td id=\"T_397eb_row5_col12\" class=\"data row5 col12\" ></td>\n",
       "      <td id=\"T_397eb_row5_col13\" class=\"data row5 col13\" ></td>\n",
       "      <td id=\"T_397eb_row5_col14\" class=\"data row5 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row6\" class=\"row_heading level0 row6\" >5.380000</th>\n",
       "      <td id=\"T_397eb_row6_col0\" class=\"data row6 col0\" >-1.93%</td>\n",
       "      <td id=\"T_397eb_row6_col1\" class=\"data row6 col1\" >0.12%</td>\n",
       "      <td id=\"T_397eb_row6_col2\" class=\"data row6 col2\" >1.90%</td>\n",
       "      <td id=\"T_397eb_row6_col3\" class=\"data row6 col3\" >-1.72%</td>\n",
       "      <td id=\"T_397eb_row6_col4\" class=\"data row6 col4\" >-2.02%</td>\n",
       "      <td id=\"T_397eb_row6_col5\" class=\"data row6 col5\" >-1.20%</td>\n",
       "      <td id=\"T_397eb_row6_col6\" class=\"data row6 col6\" >-0.18%</td>\n",
       "      <td id=\"T_397eb_row6_col7\" class=\"data row6 col7\" >0.46%</td>\n",
       "      <td id=\"T_397eb_row6_col8\" class=\"data row6 col8\" >0.28%</td>\n",
       "      <td id=\"T_397eb_row6_col9\" class=\"data row6 col9\" >1.31%</td>\n",
       "      <td id=\"T_397eb_row6_col10\" class=\"data row6 col10\" >4.29%</td>\n",
       "      <td id=\"T_397eb_row6_col11\" class=\"data row6 col11\" ></td>\n",
       "      <td id=\"T_397eb_row6_col12\" class=\"data row6 col12\" ></td>\n",
       "      <td id=\"T_397eb_row6_col13\" class=\"data row6 col13\" ></td>\n",
       "      <td id=\"T_397eb_row6_col14\" class=\"data row6 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row7\" class=\"row_heading level0 row7\" >7.530000</th>\n",
       "      <td id=\"T_397eb_row7_col0\" class=\"data row7 col0\" ></td>\n",
       "      <td id=\"T_397eb_row7_col1\" class=\"data row7 col1\" >-1.39%</td>\n",
       "      <td id=\"T_397eb_row7_col2\" class=\"data row7 col2\" >-0.67%</td>\n",
       "      <td id=\"T_397eb_row7_col3\" class=\"data row7 col3\" >-0.77%</td>\n",
       "      <td id=\"T_397eb_row7_col4\" class=\"data row7 col4\" >-1.49%</td>\n",
       "      <td id=\"T_397eb_row7_col5\" class=\"data row7 col5\" >-0.44%</td>\n",
       "      <td id=\"T_397eb_row7_col6\" class=\"data row7 col6\" >0.63%</td>\n",
       "      <td id=\"T_397eb_row7_col7\" class=\"data row7 col7\" >-0.03%</td>\n",
       "      <td id=\"T_397eb_row7_col8\" class=\"data row7 col8\" >2.14%</td>\n",
       "      <td id=\"T_397eb_row7_col9\" class=\"data row7 col9\" >4.19%</td>\n",
       "      <td id=\"T_397eb_row7_col10\" class=\"data row7 col10\" ></td>\n",
       "      <td id=\"T_397eb_row7_col11\" class=\"data row7 col11\" ></td>\n",
       "      <td id=\"T_397eb_row7_col12\" class=\"data row7 col12\" ></td>\n",
       "      <td id=\"T_397eb_row7_col13\" class=\"data row7 col13\" ></td>\n",
       "      <td id=\"T_397eb_row7_col14\" class=\"data row7 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row8\" class=\"row_heading level0 row8\" >10.540000</th>\n",
       "      <td id=\"T_397eb_row8_col0\" class=\"data row8 col0\" >0.61%</td>\n",
       "      <td id=\"T_397eb_row8_col1\" class=\"data row8 col1\" >-2.02%</td>\n",
       "      <td id=\"T_397eb_row8_col2\" class=\"data row8 col2\" >0.86%</td>\n",
       "      <td id=\"T_397eb_row8_col3\" class=\"data row8 col3\" >0.24%</td>\n",
       "      <td id=\"T_397eb_row8_col4\" class=\"data row8 col4\" >-0.33%</td>\n",
       "      <td id=\"T_397eb_row8_col5\" class=\"data row8 col5\" >0.33%</td>\n",
       "      <td id=\"T_397eb_row8_col6\" class=\"data row8 col6\" >0.36%</td>\n",
       "      <td id=\"T_397eb_row8_col7\" class=\"data row8 col7\" >1.40%</td>\n",
       "      <td id=\"T_397eb_row8_col8\" class=\"data row8 col8\" >1.14%</td>\n",
       "      <td id=\"T_397eb_row8_col9\" class=\"data row8 col9\" >2.84%</td>\n",
       "      <td id=\"T_397eb_row8_col10\" class=\"data row8 col10\" ></td>\n",
       "      <td id=\"T_397eb_row8_col11\" class=\"data row8 col11\" ></td>\n",
       "      <td id=\"T_397eb_row8_col12\" class=\"data row8 col12\" ></td>\n",
       "      <td id=\"T_397eb_row8_col13\" class=\"data row8 col13\" ></td>\n",
       "      <td id=\"T_397eb_row8_col14\" class=\"data row8 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row9\" class=\"row_heading level0 row9\" >14.760000</th>\n",
       "      <td id=\"T_397eb_row9_col0\" class=\"data row9 col0\" >0.15%</td>\n",
       "      <td id=\"T_397eb_row9_col1\" class=\"data row9 col1\" >-1.23%</td>\n",
       "      <td id=\"T_397eb_row9_col2\" class=\"data row9 col2\" >0.41%</td>\n",
       "      <td id=\"T_397eb_row9_col3\" class=\"data row9 col3\" >0.39%</td>\n",
       "      <td id=\"T_397eb_row9_col4\" class=\"data row9 col4\" >-0.94%</td>\n",
       "      <td id=\"T_397eb_row9_col5\" class=\"data row9 col5\" >1.75%</td>\n",
       "      <td id=\"T_397eb_row9_col6\" class=\"data row9 col6\" >0.39%</td>\n",
       "      <td id=\"T_397eb_row9_col7\" class=\"data row9 col7\" >1.73%</td>\n",
       "      <td id=\"T_397eb_row9_col8\" class=\"data row9 col8\" >0.68%</td>\n",
       "      <td id=\"T_397eb_row9_col9\" class=\"data row9 col9\" ></td>\n",
       "      <td id=\"T_397eb_row9_col10\" class=\"data row9 col10\" ></td>\n",
       "      <td id=\"T_397eb_row9_col11\" class=\"data row9 col11\" ></td>\n",
       "      <td id=\"T_397eb_row9_col12\" class=\"data row9 col12\" ></td>\n",
       "      <td id=\"T_397eb_row9_col13\" class=\"data row9 col13\" ></td>\n",
       "      <td id=\"T_397eb_row9_col14\" class=\"data row9 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row10\" class=\"row_heading level0 row10\" >20.660000</th>\n",
       "      <td id=\"T_397eb_row10_col0\" class=\"data row10 col0\" >-0.75%</td>\n",
       "      <td id=\"T_397eb_row10_col1\" class=\"data row10 col1\" >0.15%</td>\n",
       "      <td id=\"T_397eb_row10_col2\" class=\"data row10 col2\" >0.97%</td>\n",
       "      <td id=\"T_397eb_row10_col3\" class=\"data row10 col3\" >0.30%</td>\n",
       "      <td id=\"T_397eb_row10_col4\" class=\"data row10 col4\" >-0.79%</td>\n",
       "      <td id=\"T_397eb_row10_col5\" class=\"data row10 col5\" >1.17%</td>\n",
       "      <td id=\"T_397eb_row10_col6\" class=\"data row10 col6\" >0.13%</td>\n",
       "      <td id=\"T_397eb_row10_col7\" class=\"data row10 col7\" >-0.91%</td>\n",
       "      <td id=\"T_397eb_row10_col8\" class=\"data row10 col8\" ></td>\n",
       "      <td id=\"T_397eb_row10_col9\" class=\"data row10 col9\" ></td>\n",
       "      <td id=\"T_397eb_row10_col10\" class=\"data row10 col10\" ></td>\n",
       "      <td id=\"T_397eb_row10_col11\" class=\"data row10 col11\" ></td>\n",
       "      <td id=\"T_397eb_row10_col12\" class=\"data row10 col12\" ></td>\n",
       "      <td id=\"T_397eb_row10_col13\" class=\"data row10 col13\" ></td>\n",
       "      <td id=\"T_397eb_row10_col14\" class=\"data row10 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row11\" class=\"row_heading level0 row11\" >28.930000</th>\n",
       "      <td id=\"T_397eb_row11_col0\" class=\"data row11 col0\" >-0.47%</td>\n",
       "      <td id=\"T_397eb_row11_col1\" class=\"data row11 col1\" >-0.64%</td>\n",
       "      <td id=\"T_397eb_row11_col2\" class=\"data row11 col2\" >2.06%</td>\n",
       "      <td id=\"T_397eb_row11_col3\" class=\"data row11 col3\" >-1.50%</td>\n",
       "      <td id=\"T_397eb_row11_col4\" class=\"data row11 col4\" >-0.83%</td>\n",
       "      <td id=\"T_397eb_row11_col5\" class=\"data row11 col5\" >2.13%</td>\n",
       "      <td id=\"T_397eb_row11_col6\" class=\"data row11 col6\" >1.11%</td>\n",
       "      <td id=\"T_397eb_row11_col7\" class=\"data row11 col7\" ></td>\n",
       "      <td id=\"T_397eb_row11_col8\" class=\"data row11 col8\" ></td>\n",
       "      <td id=\"T_397eb_row11_col9\" class=\"data row11 col9\" ></td>\n",
       "      <td id=\"T_397eb_row11_col10\" class=\"data row11 col10\" ></td>\n",
       "      <td id=\"T_397eb_row11_col11\" class=\"data row11 col11\" ></td>\n",
       "      <td id=\"T_397eb_row11_col12\" class=\"data row11 col12\" ></td>\n",
       "      <td id=\"T_397eb_row11_col13\" class=\"data row11 col13\" ></td>\n",
       "      <td id=\"T_397eb_row11_col14\" class=\"data row11 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row12\" class=\"row_heading level0 row12\" >40.500000</th>\n",
       "      <td id=\"T_397eb_row12_col0\" class=\"data row12 col0\" >0.05%</td>\n",
       "      <td id=\"T_397eb_row12_col1\" class=\"data row12 col1\" >0.88%</td>\n",
       "      <td id=\"T_397eb_row12_col2\" class=\"data row12 col2\" >2.89%</td>\n",
       "      <td id=\"T_397eb_row12_col3\" class=\"data row12 col3\" >-2.03%</td>\n",
       "      <td id=\"T_397eb_row12_col4\" class=\"data row12 col4\" >-2.95%</td>\n",
       "      <td id=\"T_397eb_row12_col5\" class=\"data row12 col5\" >-1.53%</td>\n",
       "      <td id=\"T_397eb_row12_col6\" class=\"data row12 col6\" ></td>\n",
       "      <td id=\"T_397eb_row12_col7\" class=\"data row12 col7\" ></td>\n",
       "      <td id=\"T_397eb_row12_col8\" class=\"data row12 col8\" ></td>\n",
       "      <td id=\"T_397eb_row12_col9\" class=\"data row12 col9\" ></td>\n",
       "      <td id=\"T_397eb_row12_col10\" class=\"data row12 col10\" ></td>\n",
       "      <td id=\"T_397eb_row12_col11\" class=\"data row12 col11\" ></td>\n",
       "      <td id=\"T_397eb_row12_col12\" class=\"data row12 col12\" ></td>\n",
       "      <td id=\"T_397eb_row12_col13\" class=\"data row12 col13\" ></td>\n",
       "      <td id=\"T_397eb_row12_col14\" class=\"data row12 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row13\" class=\"row_heading level0 row13\" >56.690000</th>\n",
       "      <td id=\"T_397eb_row13_col0\" class=\"data row13 col0\" >-0.05%</td>\n",
       "      <td id=\"T_397eb_row13_col1\" class=\"data row13 col1\" >0.17%</td>\n",
       "      <td id=\"T_397eb_row13_col2\" class=\"data row13 col2\" >2.61%</td>\n",
       "      <td id=\"T_397eb_row13_col3\" class=\"data row13 col3\" >-1.42%</td>\n",
       "      <td id=\"T_397eb_row13_col4\" class=\"data row13 col4\" >-1.59%</td>\n",
       "      <td id=\"T_397eb_row13_col5\" class=\"data row13 col5\" ></td>\n",
       "      <td id=\"T_397eb_row13_col6\" class=\"data row13 col6\" ></td>\n",
       "      <td id=\"T_397eb_row13_col7\" class=\"data row13 col7\" ></td>\n",
       "      <td id=\"T_397eb_row13_col8\" class=\"data row13 col8\" ></td>\n",
       "      <td id=\"T_397eb_row13_col9\" class=\"data row13 col9\" ></td>\n",
       "      <td id=\"T_397eb_row13_col10\" class=\"data row13 col10\" ></td>\n",
       "      <td id=\"T_397eb_row13_col11\" class=\"data row13 col11\" ></td>\n",
       "      <td id=\"T_397eb_row13_col12\" class=\"data row13 col12\" ></td>\n",
       "      <td id=\"T_397eb_row13_col13\" class=\"data row13 col13\" ></td>\n",
       "      <td id=\"T_397eb_row13_col14\" class=\"data row13 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row14\" class=\"row_heading level0 row14\" >79.370000</th>\n",
       "      <td id=\"T_397eb_row14_col0\" class=\"data row14 col0\" >1.12%</td>\n",
       "      <td id=\"T_397eb_row14_col1\" class=\"data row14 col1\" >0.58%</td>\n",
       "      <td id=\"T_397eb_row14_col2\" class=\"data row14 col2\" >2.08%</td>\n",
       "      <td id=\"T_397eb_row14_col3\" class=\"data row14 col3\" >-1.96%</td>\n",
       "      <td id=\"T_397eb_row14_col4\" class=\"data row14 col4\" ></td>\n",
       "      <td id=\"T_397eb_row14_col5\" class=\"data row14 col5\" ></td>\n",
       "      <td id=\"T_397eb_row14_col6\" class=\"data row14 col6\" ></td>\n",
       "      <td id=\"T_397eb_row14_col7\" class=\"data row14 col7\" ></td>\n",
       "      <td id=\"T_397eb_row14_col8\" class=\"data row14 col8\" ></td>\n",
       "      <td id=\"T_397eb_row14_col9\" class=\"data row14 col9\" ></td>\n",
       "      <td id=\"T_397eb_row14_col10\" class=\"data row14 col10\" ></td>\n",
       "      <td id=\"T_397eb_row14_col11\" class=\"data row14 col11\" ></td>\n",
       "      <td id=\"T_397eb_row14_col12\" class=\"data row14 col12\" ></td>\n",
       "      <td id=\"T_397eb_row14_col13\" class=\"data row14 col13\" ></td>\n",
       "      <td id=\"T_397eb_row14_col14\" class=\"data row14 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row15\" class=\"row_heading level0 row15\" >111.120000</th>\n",
       "      <td id=\"T_397eb_row15_col0\" class=\"data row15 col0\" >0.60%</td>\n",
       "      <td id=\"T_397eb_row15_col1\" class=\"data row15 col1\" >-3.05%</td>\n",
       "      <td id=\"T_397eb_row15_col2\" class=\"data row15 col2\" >1.45%</td>\n",
       "      <td id=\"T_397eb_row15_col3\" class=\"data row15 col3\" >1.94%</td>\n",
       "      <td id=\"T_397eb_row15_col4\" class=\"data row15 col4\" ></td>\n",
       "      <td id=\"T_397eb_row15_col5\" class=\"data row15 col5\" ></td>\n",
       "      <td id=\"T_397eb_row15_col6\" class=\"data row15 col6\" ></td>\n",
       "      <td id=\"T_397eb_row15_col7\" class=\"data row15 col7\" ></td>\n",
       "      <td id=\"T_397eb_row15_col8\" class=\"data row15 col8\" ></td>\n",
       "      <td id=\"T_397eb_row15_col9\" class=\"data row15 col9\" ></td>\n",
       "      <td id=\"T_397eb_row15_col10\" class=\"data row15 col10\" ></td>\n",
       "      <td id=\"T_397eb_row15_col11\" class=\"data row15 col11\" ></td>\n",
       "      <td id=\"T_397eb_row15_col12\" class=\"data row15 col12\" ></td>\n",
       "      <td id=\"T_397eb_row15_col13\" class=\"data row15 col13\" ></td>\n",
       "      <td id=\"T_397eb_row15_col14\" class=\"data row15 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row16\" class=\"row_heading level0 row16\" >155.570000</th>\n",
       "      <td id=\"T_397eb_row16_col0\" class=\"data row16 col0\" >-0.59%</td>\n",
       "      <td id=\"T_397eb_row16_col1\" class=\"data row16 col1\" >-4.36%</td>\n",
       "      <td id=\"T_397eb_row16_col2\" class=\"data row16 col2\" >1.14%</td>\n",
       "      <td id=\"T_397eb_row16_col3\" class=\"data row16 col3\" ></td>\n",
       "      <td id=\"T_397eb_row16_col4\" class=\"data row16 col4\" ></td>\n",
       "      <td id=\"T_397eb_row16_col5\" class=\"data row16 col5\" ></td>\n",
       "      <td id=\"T_397eb_row16_col6\" class=\"data row16 col6\" ></td>\n",
       "      <td id=\"T_397eb_row16_col7\" class=\"data row16 col7\" ></td>\n",
       "      <td id=\"T_397eb_row16_col8\" class=\"data row16 col8\" ></td>\n",
       "      <td id=\"T_397eb_row16_col9\" class=\"data row16 col9\" ></td>\n",
       "      <td id=\"T_397eb_row16_col10\" class=\"data row16 col10\" ></td>\n",
       "      <td id=\"T_397eb_row16_col11\" class=\"data row16 col11\" ></td>\n",
       "      <td id=\"T_397eb_row16_col12\" class=\"data row16 col12\" ></td>\n",
       "      <td id=\"T_397eb_row16_col13\" class=\"data row16 col13\" ></td>\n",
       "      <td id=\"T_397eb_row16_col14\" class=\"data row16 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row17\" class=\"row_heading level0 row17\" >217.800000</th>\n",
       "      <td id=\"T_397eb_row17_col0\" class=\"data row17 col0\" >-2.72%</td>\n",
       "      <td id=\"T_397eb_row17_col1\" class=\"data row17 col1\" ></td>\n",
       "      <td id=\"T_397eb_row17_col2\" class=\"data row17 col2\" >-0.77%</td>\n",
       "      <td id=\"T_397eb_row17_col3\" class=\"data row17 col3\" ></td>\n",
       "      <td id=\"T_397eb_row17_col4\" class=\"data row17 col4\" ></td>\n",
       "      <td id=\"T_397eb_row17_col5\" class=\"data row17 col5\" ></td>\n",
       "      <td id=\"T_397eb_row17_col6\" class=\"data row17 col6\" ></td>\n",
       "      <td id=\"T_397eb_row17_col7\" class=\"data row17 col7\" ></td>\n",
       "      <td id=\"T_397eb_row17_col8\" class=\"data row17 col8\" ></td>\n",
       "      <td id=\"T_397eb_row17_col9\" class=\"data row17 col9\" ></td>\n",
       "      <td id=\"T_397eb_row17_col10\" class=\"data row17 col10\" ></td>\n",
       "      <td id=\"T_397eb_row17_col11\" class=\"data row17 col11\" ></td>\n",
       "      <td id=\"T_397eb_row17_col12\" class=\"data row17 col12\" ></td>\n",
       "      <td id=\"T_397eb_row17_col13\" class=\"data row17 col13\" ></td>\n",
       "      <td id=\"T_397eb_row17_col14\" class=\"data row17 col14\" ></td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_397eb_level0_row18\" class=\"row_heading level0 row18\" >304.910000</th>\n",
       "      <td id=\"T_397eb_row18_col0\" class=\"data row18 col0\" >-1.56%</td>\n",
       "      <td id=\"T_397eb_row18_col1\" class=\"data row18 col1\" ></td>\n",
       "      <td id=\"T_397eb_row18_col2\" class=\"data row18 col2\" ></td>\n",
       "      <td id=\"T_397eb_row18_col3\" class=\"data row18 col3\" ></td>\n",
       "      <td id=\"T_397eb_row18_col4\" class=\"data row18 col4\" ></td>\n",
       "      <td id=\"T_397eb_row18_col5\" class=\"data row18 col5\" ></td>\n",
       "      <td id=\"T_397eb_row18_col6\" class=\"data row18 col6\" ></td>\n",
       "      <td id=\"T_397eb_row18_col7\" class=\"data row18 col7\" ></td>\n",
       "      <td id=\"T_397eb_row18_col8\" class=\"data row18 col8\" ></td>\n",
       "      <td id=\"T_397eb_row18_col9\" class=\"data row18 col9\" ></td>\n",
       "      <td id=\"T_397eb_row18_col10\" class=\"data row18 col10\" ></td>\n",
       "      <td id=\"T_397eb_row18_col11\" class=\"data row18 col11\" ></td>\n",
       "      <td id=\"T_397eb_row18_col12\" class=\"data row18 col12\" ></td>\n",
       "      <td id=\"T_397eb_row18_col13\" class=\"data row18 col13\" ></td>\n",
       "      <td id=\"T_397eb_row18_col14\" class=\"data row18 col14\" ></td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x2da8d6f10>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B_W_Metric_raw = dataset[['difficulty', 'stability', 'p', 'y']].copy()\n",
    "B_W_Metric_raw['s_bin'] = B_W_Metric_raw['stability'].map(lambda x: round(math.pow(1.4, math.floor(math.log(x, 1.4))), 2))\n",
    "B_W_Metric_raw['d_bin'] = B_W_Metric_raw['difficulty'].map(lambda x: int(round(x)))\n",
    "B_W_Metric = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('mean').reset_index()\n",
    "B_W_Metric_count = B_W_Metric_raw.groupby(by=['s_bin', 'd_bin']).agg('count').reset_index()\n",
    "B_W_Metric['B-W'] = B_W_Metric['p'] - B_W_Metric['y']\n",
    "n = len(dataset)\n",
    "bins = len(B_W_Metric)\n",
    "B_W_Metric_pivot = B_W_Metric[B_W_Metric_count['p'] > max(50, n / (3 * bins))].pivot(index=\"s_bin\", columns='d_bin', values='B-W')\n",
    "B_W_Metric_pivot.apply(pd.to_numeric).style.background_gradient(cmap='seismic', axis=None, vmin=-0.2, vmax=0.2).format(\"{:.2%}\", na_rep='')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.3 Comparision with Anki bulit-in algorithm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def anki(history):\n",
    "    ivl = 0\n",
    "    start_ease = 2.5\n",
    "    hard_interval = 1.2\n",
    "    ease_bonus = 1.3\n",
    "    graduating_interval = 1\n",
    "    easy_interval = 4\n",
    "    new_interval = 0\n",
    "    minimum_interval = 1\n",
    "    interval_modifier = 1\n",
    "    for i, (delta_t, rating) in enumerate(history):\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item()\n",
    "        if i == 0:\n",
    "            ease = start_ease\n",
    "            if rating == 4:\n",
    "                ivl = easy_interval\n",
    "            else:\n",
    "                ivl = graduating_interval\n",
    "        else:\n",
    "            delay = delta_t - ivl\n",
    "            if delay >= 0:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = ivl * hard_interval\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = (ivl + delay // 2) * ease\n",
    "                else:\n",
    "                    ivl = (ivl + delay) * ease * ease_bonus\n",
    "                    ease += 0.15\n",
    "            # early_review\n",
    "            else:\n",
    "                if rating == 1:\n",
    "                    ivl = max(ivl * new_interval, minimum_interval)\n",
    "                    ease -= 0.2\n",
    "                elif rating == 2:\n",
    "                    ivl = max(delta_t * hard_interval / 2, ivl * hard_interval)\n",
    "                    ease -= 0.15\n",
    "                elif rating == 3:\n",
    "                    ivl = max(delta_t * ease, ivl)\n",
    "                else:\n",
    "                    ivl = max(delta_t * ease, ivl) * (0.5 * ease_bonus + 0.5)\n",
    "                    ease += 0.15\n",
    "            ivl = ivl * interval_modifier\n",
    "        ease = max(1.3, ease)\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "def sm2(history):\n",
    "    ivl = 0\n",
    "    ef = 2.5\n",
    "    reps = 0\n",
    "    for delta_t, rating in history:\n",
    "        delta_t = delta_t.item()\n",
    "        rating = rating.item() + 1\n",
    "        if rating > 2:\n",
    "            if reps == 0:\n",
    "                ivl = 1\n",
    "                reps = 1\n",
    "            elif reps == 1:\n",
    "                ivl = 6\n",
    "                reps = 2\n",
    "            else:\n",
    "                ivl = ivl * ef\n",
    "                reps += 1\n",
    "        else:\n",
    "            ivl = 1\n",
    "            reps = 0\n",
    "        ef = max(1.3, ef + (0.1 - (5 - rating) * (0.08 + (5 - rating) * 0.02)))\n",
    "        ivl = max(1, round(ivl+0.01))\n",
    "    return ivl\n",
    "\n",
    "dataset['sm2_interval'] = dataset['tensor'].map(sm2)\n",
    "dataset['sm2_p'] = np.exp(np.log(0.9) * dataset['delta_t'] / dataset['sm2_interval'])\n",
    "cross_comparison = dataset[['sm2_p', 'p', 'y']].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R_Metric:  0.03630035572356144\n",
      "R-squared: -21.5980\n",
      "RMSE: 0.1408\n",
      "[0.76478601 0.15818509]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "R_Metric = mean_squared_error(cross_comparison['y'], cross_comparison['sm2_p'], squared=False) - mean_squared_error(cross_comparison['y'], cross_comparison['p'], squared=False)\n",
    "print(\"R_Metric: \", R_Metric)\n",
    "plot_brier(dataset['sm2_p'], dataset['y'], bins=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Universal Metric of FSRS: 0.0141\n",
      "Universal Metric of SM2: 0.0669\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(6, 6))\n",
    "\n",
    "cross_comparison['SM2_B-W'] = cross_comparison['sm2_p'] - cross_comparison['y']\n",
    "cross_comparison['SM2_bin'] = cross_comparison['sm2_p'].map(lambda x: round(x, 1))\n",
    "cross_comparison['FSRS_B-W'] = cross_comparison['p'] - cross_comparison['y']\n",
    "cross_comparison['FSRS_bin'] = cross_comparison['p'].map(lambda x: round(x, 1))\n",
    "\n",
    "plt.axhline(y = 0.0, color = 'black', linestyle = '-')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='SM2_bin').agg({'y': ['mean'], 'FSRS_B-W': ['mean'], 'p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of FSRS: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['p', 'mean'], sample_weight=cross_comparison_group['p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['p', 'percent'] = cross_comparison_group['p', 'count'] / cross_comparison_group['p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['FSRS_B-W', 'mean'], s=cross_comparison_group['p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['FSRS_B-W', 'mean'], label='FSRS by SM2')\n",
    "\n",
    "cross_comparison_group = cross_comparison.groupby(by='FSRS_bin').agg({'y': ['mean'], 'SM2_B-W': ['mean'], 'sm2_p': ['mean', 'count']})\n",
    "print(f\"Universal Metric of SM2: {mean_squared_error(cross_comparison_group['y', 'mean'], cross_comparison_group['sm2_p', 'mean'], sample_weight=cross_comparison_group['sm2_p', 'count'], squared=False):.4f}\")\n",
    "cross_comparison_group['sm2_p', 'percent'] = cross_comparison_group['sm2_p', 'count'] / cross_comparison_group['sm2_p', 'count'].sum()\n",
    "plt.scatter(cross_comparison_group.index, cross_comparison_group['SM2_B-W', 'mean'], s=cross_comparison_group['sm2_p', 'percent'] * 1024, alpha=0.5)\n",
    "plt.plot(cross_comparison_group['SM2_B-W', 'mean'], label='SM2 by FSRS')\n",
    "\n",
    "plt.legend(loc='lower center')\n",
    "plt.grid(linestyle='--')\n",
    "plt.title(\"SM2 vs. FSRS\")\n",
    "plt.xlabel('Predicted R')\n",
    "plt.ylabel('B-W Metric')\n",
    "plt.xlim(0, 1)\n",
    "plt.xticks(np.arange(0, 1.1, 0.1))\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "fsrs4anki",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
